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The last few years have witnessed the emergence of data-driven weather forecast models capable of competing with – and, in some respects, outperforming – physics-based numerical models. However, recent studies have questioned the capability of data-driven models to provide reliable forecasts of extreme events. Here, we aim to evaluate this claim by comparing the performance of leading data-driven models in a semi-operational setting, focusing on the prediction of near-surface temperature and wind speed extremes globally. We find that data-driven models mostly outperform ECMWF’s physics-based deterministic model in terms of global RMSE for forecasts made 1–10 d ahead and that they can also compete in terms of extreme weather predictions in most regions. However, the performance of data-driven models varies by region, type of extreme event, and forecast lead time. Notably, data-driven models appear to perform best for temperature extremes in regions closer to the tropics and at shorter lead times. We conclude that data-driven models may already be a useful complement to physics-based forecasts in regions where they display superior tail performance but note that some challenges still need to be overcome prior to operational implementation.

Simultaneous occurrences of multiple heatwaves or cold spells in remote geographical regions have drawn considerable attention in the literature due to their potentially far-reaching impacts. We introduce a flexible toolbox to study such concurrent temperature extremes with adjustable parameters that different users can tailor to their specific needs. We then use the toolbox to present a climatological analysis of spatially compounding heatwaves and cold spells in the global midlatitudes. Specific geographical areas, such as western Russia, central Europe, southwestern Eurasia and western North America, emerge as hotspots for concurrent temperature extremes. Concurrent heatwaves are becoming more frequent, longer-lasting and more extended in the Northern Hemisphere, while the opposite holds for concurrent cold spells. Concurrent heatwaves in the Southern Hemisphere are comparatively rare but have been increasing in both number and extent. Notably, several of these trends in concurrent temperature extremes are significantly stronger than the corresponding trends in all temperature extremes.

During the first half of the Holocene (11 000 to 5000 years ago), the Northern Hemisphere experienced a strengthening of the monsoonal regime, with climate reconstructions robustly suggesting a greening of the Sahara region. Palaeoclimate archives also show that this so-called African humid period (AHP) was accompanied by changes in climate conditions at middle to high latitudes. However, inconsistencies still exist in reconstructions of the mid-Holocene (MH) climate at mid-latitudes, and model simulations provide limited support in reducing these discrepancies. In this paper, a set of simulations performed using a climate model are used to investigate the hitherto unexplored impact of Saharan greening on mid-latitude atmospheric circulation during the MH. Numerical simulations show Saharan greening has a year-round impact on the main circulation features in the Northern Hemisphere, especially during boreal summer (when the African monsoon develops). Key findings include a westward shift in the global Walker Circulation, leading to modifications in the North Atlantic jet stream in summer and the North Pacific jet stream in winter. Furthermore, Saharan greening modifies atmospheric synoptic circulation over the North Atlantic, enhancing the effect of orbital forcing on the transition of the North Atlantic Oscillation phase from predominantly positive to negative in winter and summer. Although the prescription of vegetation in the Sahara does not improve the proxy–model agreement, this study provides the first constraint on the influence of Saharan greening on northern mid-latitudes, opening new opportunities for understanding MH climate anomalies in regions such as North America and Eurasia.

The horizontal propagation of Rossby waves in the upper troposphere has been a long-standing topic in dynamical meteorology. The concept of “waveguidability’’, i.e., the capability of the background flow to act as a zonal waveguide for Rossby waves, may prove useful to address this problem, but developing a systematic definition and quantification of such a property remains challenging. With an eye to such issues, the current paper suggests a novel and efficient algorithm to solve the linearized barotropic vorticity equation on a sphere in a forced-dissipative configuration. The algorithm allows one to obtain linear wave solutions resulting from arbitrary combinations of the forcing and the background zonal wind. These solutions can be used to systematically study single- and double-jet configurations and are employed here to show that the latitude of the jet stream does not appear to affect waveguidability. The onset of barotropic instability might hinder the applicability of the linear framework, but it is shown that the nonlinear flow evolution can still be retrieved qualitatively from the linearized solution, both for the stationary component of the wave field and for the temporal evolution of transient waves.

Climate change is a global challenge with multiple far-reaching consequences, including the intensification and increased frequency of many extreme-weather events. In response to this pressing issue, we present ClimaMeter, a platform designed to assess and contextualize extreme-weather events relative to climate change. The platform offers near-real-time insights into the dynamics of extreme events, serving as a resource for researchers and policymakers while also being a science dissemination tool for the general public. ClimaMeter currently analyses heatwaves, cold spells, heavy precipitation, and windstorms. This paper elucidates the methodology, data sources, and analytical techniques on which ClimaMeter relies, providing a comprehensive overview of its scientific foundation. We further present two case studies: the late 2023 French heatwave and the July 2023 Storm Poly. We use two distinct datasets for each case study, namely Multi-Source Weather (MSWX) data, which serve as the reference for our rapid-attribution protocol, and the ERA5 dataset, widely regarded as the leading global climate reanalysis. These examples highlight both the strengths and limitations of ClimaMeter in expounding the link between climate change and the dynamics of extreme-weather events.

This study investigates the role of large-scale atmospheric processes in the development of cyclones causing extreme surface winds over the central North Atlantic basin (30 to 60° N, 10 to 50° W), focusing on the extended winter period (October–March) from 1950 until 2020 in the ERA5 reanalysis product. Extreme surface wind events are identified as footprints of spatio-temporally contiguous 10 m wind exceedances over the local 98th percentile. Cyclones that cause the top 1 % most intense wind footprints are identified. After excluding 16 (14 %) of cyclones that originated as tropical cyclones, further analysis is done on the remaining 99 extratropical cyclones (“top extremes”). These are compared to a set of cyclones yielding wind footprints with exceedances marginally above the 98th percentile (“moderate extremes”). Cyclones leading to top extremes are, from their time of cyclogenesis, characterised by the presence of pre-existing downstream cyclones, a strong polar jet, and positive upper-level potential vorticity anomalies to the north. All these features are absent or much weaker in the case of moderate extremes, implying that they play a key role in the explosive development of top extremes and in the generation of spatially extended wind footprints. There is also an indication of cyclonic Rossby wave breaking preceding the top extremes. Furthermore, analysis of the pressure tendency equation over the cyclones' evolution reveals that, although the leading contributions to surface pressure decrease vary from cyclone to cyclone, top extremes have on average a larger diabatic contribution than moderate extremes.

Co-occurring extreme climate events exacerbate adverse impacts on humans, the economy, and the environment relative to extremes occurring in isolation. While changes in the frequency of individual extreme events have been researched extensively, changes in their interactions, dependence, and joint occurrence have received far less attention, particularly in the East African region. Here, we analyse the joint occurrence of pairs of the following extremes within the same location and calendar year over East Africa: river floods, droughts, heatwaves, crop failures, wildfires and tropical cyclones. We analyse their co-occurrence on a yearly timescale because some of the climate extremes we consider play out over timescales up to several months. We use bias-adjusted impact simulations under past and future climate conditions from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). We find an increase in the area affected by pairs of these extreme events, with the strongest increases for joint heatwaves and wildfires (+940 % by the end of the century under RCP6.0 relative to present day), followed by river floods and heatwaves (+900 %) and river floods and wildfires (+250 %). The projected increase in joint occurrences typically outweighs historical increases even under an aggressive mitigation scenario (RCP2.6). We illustrate that the changes in the joint occurrences are often driven by increases in the probability of one of the events within the pairs, for instance heatwaves. The most affected locations in the East Africa region by these co-occurring events are areas close to the River Nile and parts of the Congo basin. Our results overall highlight that co-occurring extremes will become the norm rather than the exception in East Africa, even under low-end warming scenarios.

In recent years, deep learning models have rapidly emerged as a stand-alone alternative to physics-based numerical models for medium-range weather forecasting. Several independent research groups claim to have developed deep learning weather forecasts that outperform those from state-of-the-art physics-based models, and operational implementation of data-driven forecasts appears to be drawing near. However, questions remain about the capabilities of deep learning models with respect to providing robust forecasts of extreme weather. This paper provides an overview of recent developments in the field of deep learning weather forecasts and scrutinises the challenges that extreme weather events pose to leading deep learning models. Lastly, it argues for the need to tailor data-driven models to forecast extreme events and proposes a foundational workflow to develop such models.

Windstorms are among the most impacting natural hazards affecting Western and Central Europe. Information on the associated impacts and losses are essential for risk assessment and the development of adaptation and mitigation strategies. In this study, we compare reported and estimated windstorm losses from five datasets belonging to three categories: Indices combining meteorological and insurance aspects, natural hazard databases, and loss reports from insurance companies. We analyse the similarities and differences between the datasets in terms of reported events, the number of storms per dataset and the ranking of specific storm events for the period October 1999 to March 2022 across 21 European countries. A total of 94 individual windstorms were documented. Only 11 of them were reported in all five datasets, while the large majority (roughly 60%) was solely recorded in single datasets. Results show that the total number of storms is different in the various datasets, although for the meteorological indices such number is fixed a priori. Additionally, the datasets often disagree on the storm frequency per winter season. Moreover, the ranking of storms based on reported/estimated losses varies in the datasets. However, these differences are reduced when the ranking is calculated relative to storm events that are common in the various datasets. The results generally hold for losses aggregated at European and at country level. Overall, the datasets provide different views on windstorm impacts. Thus, to avoid misleading conclusions, we use no dataset as “ground truth” but treat all of them as equal. We suggest that these different views can be used to test which features are relevant for calibrating windstorm models in specific regions. Furthermore, it could enable users to assign an uncertainty range to windstorm losses. We conclude that a combination of different datasets is crucial to obtain a representative picture of windstorm associated impacts.

Background: CardioMEMS is a device suitable for telemedicine that is currently being evaluated by the Regional Health Technology Assessment (HTA) Committee of Tuscany. Two detailed HTA reports are available in the specialized literature, the results of which need to be transferred to our regional setting. These decisions in Tuscany are made by the so-called Centro Operativo HTA. Aim: To validate, with local cost-effectiveness data, the decision on CardioMEMS that will be made in the Tuscany region. Methods: Two detailed international HTA reports were rearranged and adapted to our regional setting to generate a simplified analysis that could form the basis of our decision. Two willingness-to-pay (WTP) thresholds of €20,000/quality-adjusted life year (QALY) and €50,000/QALY were considered. Results: Based on epidemiological and regulatory information, the target population in Tuscany for this device is 166 cases. The value-based price of CardioMEMS is estimated to be €4,332 and €16,662 at WTP thresholds of 20,000/QALY and 50,000/QALY, respectively. Its current price in Italy is €12,000. Conclusion: In our region, the introduction of CardioMEMS is likely to be gradual, around 50 patients/year (or €0.60 million/year at current price). This example highlights the need to adapt the information published in the international literature to the local context in which the approval decision is made. In this context, simplified analyses are easier to apply than complex Markov models.

Abstract The possibility of using selectively incorporated 19F nuclei for NMR spectroscopic studies has retrieved increasing interest in recent years. The high gyromagnetic ratio of 19F and its absence in native biomolecular systems make this nucleus an interesting alternative to standard 1H NMR spectroscopy. Here we show how we can attach a label, carrying a 19F atom, to protein tyrosines, through the use of a specific three component Mannich-type reaction. To validate the efficacy and the specificity of the approach, we tested it on two selected systems with the aid of ESI MS measurements.

In Philadelphia chromosome-positive B-cell (Ph+) acute lymphoblastic leukemia (LLA), growing evidence has accumulated regarding the efficacy of low-intensity and chemo-free regimens. Our objective was to analyze all recent trials evaluating these treatments and to compare them in terms of efficacy. We applied the Shiny method, an artificial intelligence technique, to analyze Kaplan–Meier curves and reconstruct patient-level data. Reconstructed patient data were then evaluated through standard survival statistics and subjected to indirect head-to-head treatment comparisons. The endpoint was progression-free survival (PFS). Based on 432 reconstructed patients, eight trials were analyzed. The survival data from these trials were pooled into three types of treatments: (i) treatments based on tyrosine kinase inhibitors (TKIs) combined with reduced-intensity chemotherapy (denoted as TKICHE); (ii) TKIs associated with steroids with no chemotherapy (TKISTE); (iii) chemotherapy-free combinations of blinatumomab plus TKIs (TKIBLI). According to the Shiny method, the three PFS curves were reported in a single Kaplan–Meier graph and subjected to survival statistics. In terms of PFS, TKIBLI ranked first, TKICHE second, and TKISTE third; the differences between these three regimens were statistically significant. This multi-treatment Kaplan–Meier graph, generated through the Shiny method, summarized the current evidence on these treatments in both qualitative and quantitative terms.

Auranofin (AF) is a gold-based compound with a well-known pharmacological and toxicological profile, currently used in the treatment of some severe forms of rheumatoid arthritis. Over the last twenty years, AF has also been repurposed as antiviral, antitumor, and antibacterial drug. In this review we focused on the antibacterial properties of AF, specifically researching the minimal inhibitory concentrations (MIC) of AF in both mono- and diderm bacteria reported so far in literature. AF proves to be highly effective against monoderm bacteria, while diderm are far less susceptible, probably due to the outer membrane barrier. We also reported the current mechanistic hypotheses concerning the antimicrobial properties of AF, although a conclusive description of its antibacterial mode of action is not yet available. Even if its mechanism of action has not been fully elucidated yet and further studies are required to optimize its delivery strategy, AF deserves additional investigation because of its unique mode of action and high efficacy against a wide range of pathogens, which could lead to potential applications in fighting antimicrobial resistance and improving therapeutic outcomes in infectious diseases.

We investigate the link between warm-temperature extremes in Europe and the persistence of large-scale atmospheric-circulation patterns for both winter and summer, along with some possible physical mechanisms connecting the two. We assess atmospheric persistence, leveraging concepts from dynamical systems theory, and reconcile this approach with the more conventional meteorological views of persistence. We find that wintertime warm spells are partly associated with persistent zonal advection at the surface level but display no statistically significant persistence anomaly in the mid-troposphere. For summertime heatwaves, we find a weak yet significant link to anomalously persistent circulation patterns in the mid-troposphere, while there are few significant persistence anomalies of the surface circulation pattern. We further find no evidence of a strong warm-temperature advection signal. This suggests that other radiative and dynamical processes, for example sensible heating and adiabatic warming, as well as local effects, could play a more important role than large-scale warm-temperature advection for these events. We thus argue that persistent atmospheric configurations are not a necessary requirement for warm-temperature extremes and that the results depend to a considerable extent on region and tropospheric level.

Climate extremes are unprecedented straining healthcare systems and intensifying pre-existing issues. This commentary addresses lack of (1) knowledge on unintended health consequences of adaptation actions, (2) interdisciplinary research frameworks, and (3) forward-looking, context-based scenarios. Collaboration across natural, medical, and social sciences can facilitate shifts toward more sustainable and equitable healthcare.

During the summer of 2021, the North American Pacific Northwest was affected by an extreme heatwave that broke previous temperature records by several degrees. The event caused severe impacts on human life and ecosystems, and was associated with the superposition of concurrent drivers, whose effects were amplified by climate change. We evaluate whether this record-breaking heatwave could have been foreseen prior to its observation, and how climate change affects North American Pacific Northwest worst-case heatwave scenarios. To this purpose, we use a stochastic weather generator with empirical importance sampling. The generator simulates extreme temperature sequences using circulation analogues, chosen with an importance sampling based on the daily maximum temperature over the region that recorded the most extreme impacts. We show how some of the large-scale drivers of the event can be obtained form the circulation analogues, even if such information is not directly given to the stochastic weather generator.

This paper introduces a novel approach to 4D printing tailored structures with reversible two-way shape-memory effect (SME) through material extrusion technology. To this aim, methacrylated poly(ε-caprolactone) (PCL) was synthesized and evaluated from a rheological perspective to determine its suitability for extrusion-based printing. Following a printability assessment, an optimal set of parameters was identified to fabricate 3D structures, UV-crosslinked during printing. Subsequently, a physical and thermo-mechanical characterization of the printed structures was conducted to deepen the understanding of the fabrication process and properties of the obtained structures. To assess the shape-memory properties of the printed structures, both the one-way and two-way SME under load were investigated. Overall, this study opens the floodgates to implementing 4D printing via material extrusion technology, specifically targeting PCL-based semi-crystalline chemically crosslinked polymer networks with two-way SME. Because of its cost-effectiveness, versatility, and user-friendly nature, extrusion-based printing offers noteworthy advantages over other additive manufacturing approaches when reversible behavior of the printed structures is needed. Lastly, a glimpse of potential 4D printed structures from PCL-based semi-crystalline chemically crosslinked polymer networks is presented. The approach described holds significant promise across multiple research and industrial domains, including but not limited to smart actuators, soft robotics, and medical devices.

Abstract Weather regimes have been defined over multiple regions and used in a range of practical applications, including subseasonal‐to‐seasonal forecasting and climate model evaluation. Despite their widespread use, the extent to which regimes reflect physical modes of the atmosphere is seldom investigated. Here, we adopt a year‐round classification of four North American weather regimes, with a fifth “no regime” class, and leverage dynamical systems theory to investigate their dynamical properties. We find that when the atmospheric flow is assigned to a regime, it displays persistent characteristics and a lifecycle‐like temporal evolution. We further find that, regardless of season, these characteristics are enhanced when the atmospheric flow displays a comparatively strong projection onto the cluster‐mean of the regime to which it is assigned (while the reverse is true for a weaker projection). We interpret these results as evidence that the four North American weather regimes are physically‐meaningful, with a clear dynamical footprint.

Abstract Hydrological extremes, such as droughts and floods, can trigger a complex web of compound and cascading impacts (CCI) due to interdependencies between coupled natural and social systems. However, current decision‐making processes typically only consider one impact and disaster event at a time, ignoring causal chains, feedback loops, and conditional dependencies between impacts. Analyses capturing these complex patterns across space and time are thus needed to inform effective adaptation planning. This perspective paper aims to bridge this critical gap by presenting methods for assessing the dynamics of the multi‐sector CCI of hydrological extremes. We discuss existing challenges, good practices, and potential ways forward. Rather than pursuing a single methodological approach, we advocate for methodological pluralism. We see complementary or even convergent roles for analyses based on quantitative (e.g., data‐mining, systems modeling) and qualitative methods (e.g., mental models, qualitative storylines). The data‐driven and knowledge‐driven methods provided here can serve as a useful starting point for understanding the dynamics of both high‐frequency CCI and low‐likelihood but high‐impact CCI. With this perspective, we hope to foster research on CCI to improve the development of adaptation strategies for reducing the risk of hydrological extremes.

Abstract We present an application of quantile generalized additive models (QGAMs) to study spatially compounding climate extremes, namely extremes that occur (near‐) simultaneously in geographically remote regions. We take as an example wintertime cold spells in North America and co‐occurring wet or windy extremes in Western Europe, which we collectively term Pan‐Atlantic compound extremes. QGAMS are largely novel in climate science applications and present a number of key advantages over conventional statistical models of weather extremes. Specifically, they remove the need for a direct identification and parametrization of the extremes themselves, since they model all quantiles of the distributions of interest. They thus make use of all information available, and not only of a small number of extreme values. Moreover, they do not require any a priori knowledge of the functional relationship between the predictors and the dependent variable. Here, we use QGAMs to both characterize the co‐occurrence statistics and investigate the role of possible dynamical drivers of the Pan‐Atlantic compound extremes. We find that cold spells in North America are a useful predictor of subsequent wet or windy extremes in Western Europe, and that QGAMs can predict those extremes more accurately than conventional peak‐over‐threshold models.

Background: The reconstruction of individual patient data from published Kaplan-Meier survival curves is a new technique (often denoted as the IPDfromKM method) for studying efficacy in cases where multiple trials are available, and the endpoint is long-term mortality. In patients with tricuspid regurgitation, both valve repair and valve replacement have been proposed to improve prognosis; 6 controlled clinical trials (CTs) have been conducted to compare the two therapeutic options mentioned above. The objective of our analysis was to study these six trials through the application of the IPDfromKM method. Methods: In the present report, we applied the IPDfromKM method to carry out a pooled analysis of these 6 CTs to investigate the effectiveness of valve repair vs valve replacement and to assess the between-study heterogeneity from this clinical material. After reconstructing individual patient data from these 6 trials, patients treated with valve repair were pooled together and their Kaplan-Meier curve was generated. Likewise, patients treated with valve replacement were pooled together and their Kaplan-Meier curve was generated. Finally, these two curves were compared by standard survival statistics. The hazard ratio (HR) was determined; death from any cause was the endpoint. Results: These 6 CTs included a total of 552 patients; in each of these CTs, the patient group treated with valve repair was compared with another group treated with valve replacement. Our statistical results showed a significantly better survival for valve repair compared with valve replacement (HR, 0.6098; 95% confidence intervals (CI), 0.445 to 0.835; p = 0.002). Heterogeneity was found to be significant in the 6 patient arms undergoing replacement, but not in those undergoing valve repair. In valve replacement, the classification of patients in class III or IV of New York Heart Association (NYHA) was the main negative prognostic factor. Conclusions: Our analysis confirmed the methodological advantages of the IPDfromKM method in the indirect comparative analysis of multiple trials. These advantages include appropriate analysis of censored patients, original assessment of heterogeneity, and graphical presentation of the results, wherein individual patients retain an important role.

Recent research has established a statistical link between North American cold spells (CS) and concurrent wet or windy extremes in Europe. Here, we investigate whether such a link can be related to changes in the characteristics of North Atlantic extratropical cyclones (hereafter cyclones). Despite large regions of anomalous baroclinicity during periods of North American CS, the number of cyclones across the North Atlantic as a whole is not significantly above climatology. However, we observe rates of explosive cyclogenesis significantly above climatology. We further find that CS over different North American regions are associated with large-scale atmospheric configurations displaying a strenghtened jet stream in the vicinity of the CS and different upper-level wind anomalies in the East Atlantic. These, in turn, modulate the regional distributions and characteristics of cyclones in the North Atlantic and Europe. We compute a fixed-radius cyclone footprint which we use to associate extreme precipitation and wind occurrences to individual cyclones. For eastern Canada CS, the North Atlantic jet extends over Northern Europe, resulting in a heightened numbers of cyclones for the British Isles and Scandinavia, while France, the British Isles, Northern Europe and Scandinavia all experience more intense cyclones. The British Isles consistently experience wind extremes associated with cyclones during east Canada CS. For central Canada CS, the jet is displaced poleward and only partly extends over Europe, resulting in an above average cyclone density in the eastern Atlantic and a higher number of cyclones affecting Iberia only. Iberia consistently experiences precipitation extremes associated with the cyclones during central Canada CS. For eastern United States CS, the jet is displaced equatorward and extends over Southern Europe, with a significantly heightened number of cyclones affecting Iberia and France. Iberia experiences wind extremes associated with the cyclones during eastern United States CS. Our results provide a dynamical explanation for previous statistical findings on the concurrence of North American CS and wet or windy extremes over Europe.

Abstract Weather regimes are recurrent and quasi‐stationary large‐scale atmospheric circulation patterns, typically linking to surface weather. Two commonly used sets of weather regimes are wintertime North American and Euro‐Atlantic regimes. Notwithstanding recent evidence pointing to a connection between winter weather in North America and Europe, there is little knowledge on the possible relation between North American and Euro‐Atlantic regimes. Here, we find that specific pairs of North American and Euro‐Atlantic regimes show a close visual and statistical correspondence. Moreover, the joint analysis of the two sets of regimes can provide medium‐range statistical predictability for anomalies in their occurrence frequencies. Conditioning on North American weather regimes also results in anomalies in both the large‐scale circulation during specific Euro‐Atlantic regimes, and the associated European surface weather. We conclude that there is a benefit in conducting joint analyses of North American and European weather regimes, as opposed to considering the two in isolation.

What role can a speculative political ecology play in (re)imaging urban futures of climate extremes? In recent years, narratives of dystopian futures of climate extremes have proliferated in geosciences, and across the media and creative arts. These anxiety-fueled narratives often generate a sense of resignation and unavoidability, which contributes to foreclosing the possibility of radically different political projects. In this article, we argue that these narratives conceal the coproduction of nature and society and treat nature as the problem, thereby locking futures into dystopic configurations. Political ecology scholarship can contribute to generate a politics of possibility by reconceptualizing the relations that constitute urban futures under climate extremes as socionatural. This, we argue, calls for a more experimental political ecology and new forms of theorizing. To this aim, we develop a speculative political ecological approach grounded on a numerical model that examines the potential of transformative change in the aftermath of extreme flood events in a capitalist city. Analytically, this opens a unique possibility of exploring urban futures beyond current trajectories, and how these alternative futures might transform vulnerability and inequality across urban spaces. From a policy perspective, we lay the foundations for a new generation of models that apprehend the role of power and agency in shaping uneven urban futures of climate extremes.

Introduction: In this retrospective case-series study we discuss the clinical and radiographic outcomes obtained following the “tibia-first concept” in the treatment of distal tibia fractures, both in patients with fibular comminution and in cases with a simple fibula fracture. Materials and methods: We analysed a consecutive series of 64 patients who presented at our emergency department with a distal articular tibial and fibular fracture from January 2015 to September 2020. A total of 22 patients met the inclusion and exclusion criteria and were included in the study. Clinical and radiographic examination were performed at each follow-up. To quantify pain and functional disability, the Foot and Ankle Outcome Score (FAOS) and the American Orthopaedic Foot and Ankle Society’s ankle- hindfoot scale (AOFAS) were applied. Results: The overall mean age was 52.8 years, and the mean follow-up was 13.18 months. Multiple scales data from the FAOS were as follows: pain score 80.70; symptoms score 81.69; activities of daily living score 87.22; quality of life 76.05. The mean AOFAS ankle-hindfoot score was 74.36. Conclusions: Even though the principles of Rüedi and Allgöwer are still valid, in specific circumstances, the tibia- first concept could be considered as a valid option for the treatment of these demanding fractures. If a good reduction is obtained intra-operatively by ligamentotaxis, we recommend fixing the tibia first, avoiding surgical stress on tissues derived from a previous fibular fixation.

Abstract Background In patients with advanced melanoma, immune‐checkpoint inhibitors (ICIs) represent the mainstay for first line treatment. Recently, relatlimab+nivolumab was proposed as a new combination therapy. This review was aimed at summarizing the current data of effectiveness for ICIs. Progression‐free survival (PFS) was the endpoint of our analysis. Methods After a standard literature search, Phase II/III studies comparing different ICI regimens in previously untreated advanced melanoma patients were analyzed. Patient‐level data were reconstructed from Kaplan–Meier curves by application of the IPDfromKM method. These reconstructed datasets were used to perform indirect comparisons between treatments. Standard statistical testing was used, including hazard ratio and medians. A secondary analysis employed the restricted mean survival time. Results Six trials were included in our analysis. Information on PFS from these trials was pooled according to the following treatments: nivolumab or pembrolizumab as monotherapy, or in combination with ipilimumab, and relatlimab + nivolumab. Pembrolizumab+ipilimumab showed significantly better PFS compared with the other treatments; nivolumab+ipilimumab ranked second; the other treatments showed a similar survival pattern. Conclusions The picture of comparative effectiveness resulting from our analysis is complex. The IPDfromKM method is advantageous because it accounts for the length of follow‐up but loses the balance between treatment group and controls determined by randomization. Based on indirect comparisons, the combination of pembrolizumab+ipilimumab showed a particularly high efficacy, and so deserves further investigation. While the effect of between‐trial differences in inclusion criteria plays an important role, our results do not support the proposal of relatlimab+nivolumab as a new standard of care.

In recent years, new treatments have been studied for relapsed-refractory multiple myeloma (RRMM), including two CAR-T products and a variety of non-CAR-T agents. Since direct comparisons between these innovative treatments are not available, indirect comparisons can be of interest. Reconstruction of individual patient data from Kaplan-Meier graphs (e.g., according to the Shiny method) has been the subject of numerous reports that have fully validated their performance. In the present systematic review, we evaluated six treatments proposed for RRMM, including two CAR-T products (ciltacabtagene autoleucel and idecabtagene vicleucel) and four treatments not based on a CAR-T (melflufen plus dexamethasone, isatuximab plus dexamethasone, selinexor, and belantamab). The endpoint was overall survival (OS). Our results showed statistically significant differences in OS across these treatments. In particular, ciltacabtagene autoleucel showed better OS than idecabtagene vicleucel. As regards non-CAR-T treatments, the ranking in OS was headed by isatuximab plus dexamethasone, followed by belantamab, selinexor, and melflufen plus dexamethasone. In conclusion, while the Shiny method has confirmed its validity in reconstructing individual patient data, our indirect comparisons have offered some original clues to interpret the results of OS published in these studies.