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Background Natural language processing could assist multiple tasks in systematic reviews to reduce workflow, including the extraction of PICO elements such as study populations, interventions, comparators and outcomes. The PICO framework provides a basis for the retrieval and selection for inclusion of evidence relevant to a specific systematic review question, and automatic approaches to PICO extraction have been developed particularly for reviews of clinical trial findings. Considering the difference between preclinical animal studies and clinical trials, developing separate approaches is necessary. Facilitating preclinical systematic reviews will inform the translation from preclinical to clinical research. Methods We randomly selected 400 abstracts from the PubMed Central Open Access database which described in vivo animal research and manually annotated these with PICO phrases for Species, Strain, methods of Induction of disease model, Intervention, Comparator and Outcome. We developed a two-stage workflow for preclinical PICO extraction. Firstly we fine-tuned BERT with different pre-trained modules for PICO sentence classification. Then, after removing the text irrelevant to PICO features, we explored LSTM-, CRF- and BERT-based models for PICO entity recognition. We also explored a self-training approach because of the small training corpus. Results For PICO sentence classification, BERT models using all pre-trained modules achieved an F1 score of over 80%, and models pre-trained on PubMed abstracts achieved the highest F1 of 85%. For PICO entity recognition, fine-tuning BERT pre-trained on PubMed abstracts achieved an overall F1 of 71% and satisfactory F1 for Species (98%), Strain (70%), Intervention (70%) and Outcome (67%). The score of Induction and Comparator is less satisfactory, but F1 of Comparator can be improved to 50% by applying self-training. Conclusions Our study indicates that of the approaches tested, BERT pre-trained on PubMed abstracts is the best for both PICO sentence classification and PICO entity recognition in the preclinical abstracts. Self-training yields better performance for identifying comparators and strains.

Background Time-to-event data is frequently reported in both clinical and preclinical research spheres. Systematic review and meta-analysis is a tool that can help to identify pitfalls in preclinical research conduct and reporting that can help to improve translational efficacy. However, pooling of studies using hazard ratios (HRs) is cumbersome especially in preclinical meta-analyses including large numbers of small studies. Median survival is a much simpler metric although because of some limitations, which may not apply to preclinical data, it is generally not used in survival meta-analysis. We aimed to appraise its performance when compared with hazard ratio-based meta-analysis when pooling large numbers of small, imprecise studies. Methods We simulated a survival dataset with features representative of a typical preclinical survival meta-analysis, including with influence of a treatment and a number of covariates. We calculated individual patient data-based hazard ratios and median survival ratios (MSRs), comparing the summary statistics directly and their performance at random-effects meta-analysis. Finally, we compared their sensitivity to detect associations between treatment and influential covariates at meta-regression. Results There was an imperfect correlation between MSR and HR, although the opposing direction of treatment effects between summary statistics appeared not to be a major issue. Precision was more conservative for HR than MSR, meaning that estimates of heterogeneity were lower. There was a slight sensitivity advantage for MSR at meta-analysis and meta-regression, although power was low in all circumstances. Conclusions We believe we have validated MSR as a summary statistic for use in a meta-analysis of small, imprecise experimental survival studies—helping to increase confidence and efficiency in future reviews in this area. While assessment of study precision and therefore weighting is less reliable, MSR appears to perform favourably during meta-analysis. Sensitivity of meta-regression was low for this set of parameters, so pooling of treatments to increase sample size may be required to ensure confidence in preclinical survival meta-regressions.

Helping every scientist to improve is more effective than ferreting out a few frauds. Helping every scientist to improve is more effective than ferreting out a few frauds.

Stroke is the second highest cause of death globally and a leading cause of disability, with an increasing incidence in developing countries. Ischaemic stroke caused by arterial occlusion is responsible for the majority of strokes. Management focuses on rapid reperfusion with intravenous thrombolysis and endovascular thrombectomy, which both reduce disability but are time-critical. Accordingly, improving the system of care to reduce treatment delays is key to maximizing the benefits of reperfusion therapies. Intravenous thrombolysis reduces disability when administered within 4.5 h of the onset of stroke. Thrombolysis also benefits selected patients with evidence from perfusion imaging of salvageable brain tissue for up to 9 h and in patients who awake with stroke symptoms. Endovascular thrombectomy reduces disability in a broad group of patients with large vessel occlusion when performed within 6 h of stroke onset and in patients selected by perfusion imaging up to 24 h following stroke onset. Secondary prevention of ischaemic stroke shares many common elements with cardiovascular risk management in other fields, including blood pressure control, cholesterol management and antithrombotic medications. Other preventative interventions are tailored to the mechanism of stroke, such as anticoagulation for atrial fibrillation and carotid endarterectomy for severe symptomatic carotid artery stenosis.

In recent years there has been increasing concern about the rigor of laboratory research. Here we present the protocol for a study comparing the completeness of reporting of in vivo and in vitro research carried in Nature Publication Group journals before and after the introduction of a change in editorial policy (the introduction of a set of guidelines for reporting); and in similar research published in other journals in the same periods. Electronic supplementary material The online version of this article (doi:10.1007/s11192-016-1964-8) contains supplementary material, which is available to authorized users.

Cancer pain is not a single entity but a complex pain state involving different pain syndromes, with inflammatory, neuropathic, compressive, and ischaemic mechanisms. Current therapeutic regimens are based largely on opioids, although opioid treatment can lead to many side effects. Studies using animal models of cancer pain are aimed at understanding cancer pain and developing novel therapies. The most frequently reported models are of bone cancer pain, predominantly modelling pain associated with tumour growth within bone marrow. Here we summarise recent findings from studies using animal models of cancer pain and discuss the methodological quality of these studies.