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This paper offers a review of the origins, design strategy and implementation plans of the Teacher Education in Sub-Saharan Africa (TESSA) research and development programme. The programme is working to develop new models of teacher education, particularly school based training, including the creation of a programme webspace and an extensive bank of Open Educational Resources. This paper identifies key research findings and literature which informed the TESSA approach and activity design. Drawing on participant experiences in different development activities and data generated in development testing activities, I offer a personal account of the programme to date. The paper concludes by suggesting a pattern of resource making and design that could be adopted by other programmes serving parallel development needs.Editors: Andreia Santos (Open University, UK). Reviewers: Richard F. Heller (Peoples-uni.org, UK) and Andreia Santos (Open U., UK).

This article presents the results of research that explored the relationship between emotional intelligence and learner autonomy in the context of nontraditional higher education and their impact on student retention. This was predicated on previous research that suggested emotional intelligence might lead to student success and that autonomous learners persevere toward academic goals with more resourcefulness, initiative, and persistence than other learners do. For this study, 129 nontraditional undergraduate students associated with a small, private, liberal arts college in the northeastern U.S. completed web-based surveys measuring emotional intelligence and learner autonomy. Results indicate a strong relationship between emotional intelligence and learner autonomy. Two of three emotional intelligence constructs and two of four learner autonomy constructs contributed to retention in a logistic regression model. The article concludes with discussion of limitations of the study, suggestions for future research, and how administrators of nontraditional programs can benefit from this study. (Contains 2 figures and 3 tables.)

Summary: Cancellations within 24 h of planned elective surgical procedures reduce operating theatre efficiency, add unnecessary costs and negatively affect patient experience. We implemented a bundle intervention that aimed to reduce same‐day case cancellations. This consisted of communication tools to improve patient engagement and new screening instruments (automated estimation of ASA physical status and case cancellation risk score plus four screening questions) to identify patients in advance (ideally before case booking) who needed comprehensive pre‐operative risk stratification. We studied patients scheduled for ambulatory surgery with the otorhinolaryngology service at a single centre from April 2021 to December 2022. Multivariable logistic regression and interrupted time‐series analyses were used to analyse the effects of this intervention on case cancellations within 24 h and costs. We analysed 1548 consecutive scheduled cases. Cancellation within 24 h occurred in 114 of 929 (12.3%) cases pre‐intervention and 52 of 619 (8.4%) cases post‐intervention. The cancellation rate decreased by 2.7% (95%CI 1.6–3.7%, p < 0.01) during the first month, followed by a monthly decrease of 0.2% (95%CI 0.1–0.4%, p < 0.01). This resulted in an estimated $150,200 (£118,755; €138,370) or 35.3% cost saving (p < 0.01). Median (IQR [range]) number of days between case scheduling and day of surgery decreased from 34 (21–61 [0–288]) pre‐intervention to 31 (20–51 [1–250]) post‐intervention (p < 0.01). Patient engagement via the electronic health record patient portal or text messaging increased from 75.9% at baseline to 90.8% (p < 0.01) post‐intervention. The primary reason for case cancellation was patients' missed appointment on the day of surgery, which decreased from 7.2% pre‐intervention to 4.5% post‐intervention (p = 0.03). An anaesthetist‐driven, clinical informatics‐based bundle intervention decreases same‐day case cancellation rate and associated costs in patients scheduled for ambulatory otorhinolaryngology surgery. [ABSTRACT FROM AUTHOR]

Objectives We aim to evaluate the accuracy of radiologists and radiology residents in the detection of paediatric appendicular fractures with and without the help of a commercially available fracture detection artificial intelligence (AI) solution in the hopes of showing potential clinical benefits in a general hospital setting.Methods This was a retrospective study involving three associate consultants (AC) and three senior residents (SR) in radiology, who acted as readers. One reader from each human group interpreted the radiographs with the aid of AI. Cases were categorised into concordant and discordant cases between each interpreting group. Discordant cases were further evaluated by three independent subspecialty radiology consultants to determine the final diagnosis. A total of 500 anonymised paediatric patient cases (aged 2–15 years) who presented to a tertiary general hospital with a Children’s emergency were retrospectively collected. Main outcome measures include the presence of fracture, accuracy of readers with and without AI, and total time taken to interpret the radiographs.Results The AI solution alone showed the highest accuracy (area under the receiver operating characteristic curve 0.97; AC: 95% CI −0.055 to 0.320, p=0; SR: 95% CI 0.244 to 0.598, p=0). The two readers aided with AI had higher area under curves compared with readers without AI support (AC: 95% CI −0.303 to 0.465, p=0; SR: 95% CI −0.154 to 0.331, p=0). These differences were statistically significant.Conclusion Our study demonstrates excellent results in the detection of paediatric appendicular fractures using a commercially available AI solution. There is potential for the AI solution to function autonomously.

The considerable breadth of the subject and the innumerable options for framing the task pose a daunting challenge to anyone writing an introductory social work text. Given virtually infinite possibilities, [...]

American historian Henry Brooks Adams initiated his life-spanning historical 'study in unity' in Mont-Saint-Michel and Chartres, and began studying history in relation to 'fixed points' in The Education of Henry Adams. He intended using his studies of the points of 'Thirteenth-Century Unity' and 'Twentieth-Century Multiplicity'--Europe, or medieval unity (the Virgin), and the Frontier, or modern multiplicity (the Dynamo, or Industrialization)--to develop a 'science of history', applying evolutionary theories to history. Adams hoped that this new science would allow historians, using factual documents and papers as evidence, to understand the present and the past, and predict the future, and to demonstrate that 'there could be no such thing as a stable equilibrium when phenomena are irreversible and events are random.' Even after Henry Adams began experiencing what he called 'inevitable isolation and disillusionment', he continued to insist that the search for a science of history must not be abandoned, believing that '[s]cience itself would admit its own failure if it admitted that man, the most important of all its subjects, could not be brought within its range.' Henry Adams' tragedy was that he never realized he had found the truth he sought. [ABSTRACT FROM AUTHOR]

Purpose: We sought to develop and validate an Anticipated Surveillance Requirement Prediction Instrument (ASRI) for prediction of prolonged postanesthesia care unit length of stay (PACU-LOS, more than four hours) after ambulatory surgery., Methods: We analyzed hospital registry data from patients who received anesthesia care in ambulatory surgery centres (ASCs) of university-affiliated hospital networks in New York, USA (development and internal validation cohort [n = 183,711]) and Massachusetts, USA (validation cohort [n = 148,105]). We used stepwise backwards elimination to create ASRI., Results: The model showed discriminatory ability in the development, internal, and external validation cohorts with areas under the receiver operating characteristic curve of 0.82 (95% confidence interval [CI], 0.82 to 0.83), 0.82 (95% CI, 0.81 to 0.83), and 0.80 (95% CI, 0.79 to 0.80), respectively. In cases started in the afternoon, ASRI scores ≥ 43 had a total predicted risk for PACU stay past 8 p.m. of 32% (95% CI, 31.1 to 33.3) vs 8% (95% CI, 7.9 to 8.5) compared with low score values (P-for-interaction < 0.001), which translated to a higher direct PACU cost of care of USD 207 (95% CI, 194 to 2,019; model estimate, 1.68; 95% CI, 1.64 to 1.73; P < 0.001) The effects of using the ASRI score on PACU use efficiency were greater in a free-standing ASC with no limitations on PACU bed availability., Conclusion: We developed and validated a preoperative prediction tool for prolonged PACU-LOS after ambulatory surgery that can be used to guide scheduling in ambulatory surgery to optimize PACU use during normal work hours, particularly in settings without limitation of PACU bed availability., (© 2023. Canadian Anesthesiologists' Society.)

Objective: The ASA physical status (ASA-PS) is determined by an anesthesia provider or surgeon to communicate co-morbidities relevant to perioperative risk. Assigning an ASA-PS is a clinical decision and there is substantial provider-dependent variability. We developed and externally validated a machine learning-derived algorithm to determine ASA-PS (ML-PS) based on data available in the medical record., Design: Retrospective multicenter hospital registry study., Setting: University-affiliated hospital networks., Patients: Patients who received anesthesia at Beth Israel Deaconess Medical Center (Boston, MA, training [n = 361,602] and internal validation cohorts [n = 90,400]) and Montefiore Medical Center (Bronx, NY, external validation cohort [n = 254,412])., Measurements: The ML-PS was created using a supervised random forest model with 35 preoperatively available variables. Its predictive ability for 30-day mortality, postoperative ICU admission, and adverse discharge were determined by logistic regression., Main Results: The anesthesiologist ASA-PS and ML-PS were in agreement in 57.2% of the cases (moderate inter-rater agreement). Compared with anesthesiologist rating, ML-PS assigned more patients into extreme ASA-PS (I and IV), (p < 0.01), and less patients in ASA II and III (p < 0.01). ML-PS and anesthesiologist ASA-PS had excellent predictive values for 30-day mortality, and good predictive values for postoperative ICU admission and adverse discharge. Among the 3594 patients who died within 30 days after surgery, net reclassification improvement analysis revealed that using the ML-PS, 1281 (35.6%) patients were reclassified into the higher clinical risk category compared with anesthesiologist rating. However, in a subgroup of multiple co-morbidity patients, anesthesiologist ASA-PS had a better predictive accuracy than ML-PS., Conclusions: We created and validated a machine learning physical status based on preoperatively available data. The ability to identify patients at high risk early in the preoperative process independent of the provider's decision is a part of the process we use to standardize the stratified preoperative evaluation of patients scheduled for ambulatory surgery., Competing Interests: Declaration of Competing Interest All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version. This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue. The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript. The following authors have affiliations with organizations with direct or indirect financial interest in the subject matter discussed in the manuscript: Matthias Eikermann received unrestricted funds from philanthropic donors Jeffrey and Judith Buzen, (Copyright © 2023 Elsevier Inc. All rights reserved.)

Objective: Avoidable case cancellations within 24 h reduce operating room (OR) efficiency, add unnecessary costs, and may have physical and emotional consequences for patients and their families. We developed and validated a prediction tool that can be used to guide same day case cancellation reduction initiatives., Design: Retrospective hospital registry study., Setting: University-affiliated hospitals network (NY, USA)., Patients: 246,612 (1/2016-6/2021) and 58,662 (7/2021-6/2022) scheduled elective procedures were included in the development and validation cohort., Measurements: Case cancellation within 24 h was defined as cancelling a surgical procedure within 24 h of the scheduled date and time. Our candidate predictors were defined a priori and included patient-, procedural-, and appointment-related factors. We created a prediction tool using backward stepwise logistic regression to predict case cancellation within 24 h. The model was subsequently recalibrated and validated in a cohort of patients who were recently scheduled for surgery., Main Results: 8.6% and 8.7% scheduled procedures were cancelled within 24 h of the intended procedure in the development and validation cohort, respectively. The final weighted score contains 29 predictors. A cutoff value of 15 score points predicted a 10.3% case cancellation rate with a negative predictive value of 0.96, and a positive predictive value of 0.21. The prediction model showed good discrimination in the development and validation cohort with an area under the receiver operating characteristic curve (AUC) of 0.79 (95% confidence interval 0.79-0. 80) and an AUC of 0.73 (95% confidence interval 0.72-0.73), respectively., Conclusions: We present a validated preoperative prediction tool for case cancellation within 24 h of surgery. We utilize the instrument in our institution to identify patients with high risk of case cancellation. We describe a process for recalibration such that other institutions can also use the score to guide same day case cancellation reduction initiatives., Competing Interests: Declaration of Competing Interest None., (Copyright © 2022. Published by Elsevier Inc.)

The PI3K pathway is considered a master regulator for cancer due to its frequent activation, making it an attractive target for pharmacologic intervention. While substantial efforts have been made to develop drugs targeting PI3K signaling, few drugs have been able to achieve the inhibition necessary for effective tumor control at tolerated doses. HSP90 is a chaperone protein that is overexpressed and activated in many tumors and as a consequence, small-molecule ligands of HSP90 are preferentially retained in tumors up to 20 times longer than in normal tissue. We hypothesize that the generation of conjugates that use a HSP90-targeting ligand and a payload such as copanlisib, may open the narrow therapeutic window of this and other PI3K inhibitors. In support of this hypothesis, we have generated a HSP90-PI3K drug conjugate, T-2143 and utilizing xenograft models, demonstrate rapid and sustained tumor accumulation of the conjugate, deep pathway inhibition, and superior efficacy than the PI3K inhibitor on its own. Selective delivery of T-2143 and the masking of the inhibitor active site was also able to mitigate a potentially dose-limiting side effect of copanlisib, hyperglycemia. These data demonstrate that by leveraging the preferential accumulation of HSP90-targeting ligands in tumors, we can selectively deliver a PI3K inhibitor leading to efficacy in multiple tumor models without hyperglycemia in mice. These data highlight a novel drug delivery strategy that allows for the potential opening of a narrow therapeutic window through specific tumor delivery of anticancer payloads and reduction of toxicity., (©2020 American Association for Cancer Research.)