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Incomplete reporting of research limits its usefulness and contributes to research waste. Numerous reporting guidelines have been developed to support complete and accurate reporting of health-care research studies. Completeness of reporting can be measured by evaluating the adherence to reporting guidelines. However, assessing adherence to a reporting guideline often lacks uniformity. In 2019, we developed a reporting adherence tool for the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement. With recent advances in regression and artificial intelligence (AI)/machine learning (ML)–based methods, TRIPOD + AI (www.tripod-statment.org) was developed to replace the TRIPOD statement. The aim of this study was to develop an updated adherence tool for TRIPOD + AI. Based on the TRIPOD + AI full reporting guideline, including the accompanying explanation and elaboration light, and TRIPOD + AI for abstracts, we updated and expanded the original TRIPOD adherence tool and refined the adherence elements and their scoring rules through discussions within the author team and a pilot test. The updated tool comprises of 37 main items and 136 adherence elements and includes several automated scoring rules. We developed separate TRIPOD + AI adherence tools for model development, model evaluation, and for studies describing both in a single paper. A uniform approach to assessing reporting adherence of TRIPOD + AI allows for comparisons across various fields, monitor reporting over time, and incentivizes primary study authors to comply. Accurate and complete reporting is crucial in biomedical research to ensure findings can be effectively used. To support researchers in reporting their findings well, reporting guidelines have been developed for different study types. One such guideline is TRIPOD, which focuses on research studies about medical prediction tools. In 2024, TRIPOD was updated to TRIPOD + AI to address the increasing use of AI and ML in prediction model studies. In 2019, we developed a scoring system to evaluate how well research papers on prediction tools adhered to the TRIPOD guideline, resulting in a reporting completeness score. This score allows for easier comparison of reporting completeness across various medical fields, and to monitor improvement in reporting over time. With the introduction of TRIPOD + AI, an update of the scoring system was required to align with the new reporting recommendations. We achieved this by reviewing our previous scoring system and incorporating the new items from TRIPOD + AI to better suit studies involving AI. We believe that this system will facilitate comparisons of prediction model reporting completeness across different fields and encourage improved reporting practices. •Reporting guidelines have been developed for various study types.•Recent guidelines address the increasing use of AI/ML in prediction modelling.•Scoring systems have been developed to evaluate adherence to the specific guidelines.•Using such a system encourages good reporting practices.

Background While many studies have consistently found incomplete reporting of regression-based prediction model studies, evidence is lacking for machine learning-based prediction model studies. We aim to systematically review the adherence of Machine Learning (ML)-based prediction model studies to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement. Methods We included articles reporting on development or external validation of a multivariable prediction model (either diagnostic or prognostic) developed using supervised ML for individualized predictions across all medical fields. We searched PubMed from 1 January 2018 to 31 December 2019. Data extraction was performed using the 22-item checklist for reporting of prediction model studies ( www.TRIPOD-statement.org ). We measured the overall adherence per article and per TRIPOD item. Results Our search identified 24,814 articles, of which 152 articles were included: 94 (61.8%) prognostic and 58 (38.2%) diagnostic prediction model studies. Overall, articles adhered to a median of 38.7% (IQR 31.0–46.4%) of TRIPOD items. No article fully adhered to complete reporting of the abstract and very few reported the flow of participants (3.9%, 95% CI 1.8 to 8.3), appropriate title (4.6%, 95% CI 2.2 to 9.2), blinding of predictors (4.6%, 95% CI 2.2 to 9.2), model specification (5.2%, 95% CI 2.4 to 10.8), and model’s predictive performance (5.9%, 95% CI 3.1 to 10.9). There was often complete reporting of source of data (98.0%, 95% CI 94.4 to 99.3) and interpretation of the results (94.7%, 95% CI 90.0 to 97.3). Conclusion Similar to prediction model studies developed using conventional regression-based techniques, the completeness of reporting is poor. Essential information to decide to use the model (i.e. model specification and its performance) is rarely reported. However, some items and sub-items of TRIPOD might be less suitable for ML-based prediction model studies and thus, TRIPOD requires extensions. Overall, there is an urgent need to improve the reporting quality and usability of research to avoid research waste. Systematic review registration PROSPERO, CRD42019161764.

Background: Loss of subtalar (ST) motion after arthrodesis for cavovarus can alter loads through the ankle. In this context, first ray position has the potential to protect or further overload the ankle joint. This study’s purpose was to assess the influence of first ray plantarflexion on tibiotalar cartilage contact mechanics in a native ST joint and following ST arthrodesis. Methods: Twelve below-knee cadaveric specimens were mechanically loaded to simulate 2-legged standing (neutral ankle flexion, 600 N axial load, 45 N of Achilles tendon tension). A piezoresistive pressure sensor measured cartilage contact pressure in the loaded ankle joint both before and after ST fusion, and before and after application of a 4-mm or 8-mm dorsal opening wedge (Cotton osteotomy) in each fusion condition. Peak and mean contact pressure, contact area, and center of pressure were compared between ST-fused and unfused conditions with each first-ray correction. Results: Peak pressure in the unfused ST condition moved anteromedially and increased slightly over baseline by an average of 4% ± 11.5% and 11% ± 17.9% with the 4-mm and 8-mm wedges, respectively. Relative to the unfused baseline conditions, ST fusion lateralized and decreased joint contact area by an average of 18% ± 9.3% (p < 0.001). This resulted in significantly increased peak (32% ± 21.7%, 38% ± 23.4%, and 49% ± 30.5%, P < .05) and mean contact pressures (23% ± 22.2%, 23% ± 19.8%, 21% ± 19.1%, P < .05) for the fused baseline, fused 4 mm, and fused 8 mm, respectively. Conclusion: Overall, ST fusion had a greater effect on ankle contact pressures than changes in first-ray position. ST fusion combined with increasing plantarflexion of the first ray shifted peak contact stress anteromedially. These findings, although speculative because of the utilization of non-deformed specimens, and the use of a static loading model that does not account for dynamic muscle forces during gait, are important in the setting of a ST arthrodesis for forefoot driven cavus. As the forefoot assumes a more cavus position in the absence of ST motion, the increased contact stress may put the ankle joint at higher risk of arthritic progression. Clinical Relevance: These findings suggest that subtalar arthrodesis inherently increases tibiotalar contact stresses, potentially predisposing the ankle to degenerative changes regardless of the degree of first ray correction.

COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.

This study investigates the seismic response of a horizontal axis wind turbine on two bottom-fixed support structures for transitional water depths (30-60 m), a tripod and a jacket, both resting on pile foundations. Fully coupled, nonlinear time-domain simulations on full system models are carried out under combined wind-wave-earthquake loadings, for different load cases, considering fixed and flexible foundation models. It is shown that earthquake loading may cause a significant increase of stress resultant demands, even for moderate peak ground accelerations, and that fully coupled nonlinear time-domain simulations on full system models are essential to capture relevant information on the moment demand in the rotor blades, which cannot be predicted by analyses on simplified models allowed by existing standards. A comparison with some typical design load cases substantiates the need for an accurate seismic assessment in sites at risk from earthquakes.

Background Reporting of COVID-19 prognostic models frequently falls short of established standards. The TRIPOD checklist and its 2024 AI extension (TRIPOD + AI) provide a comprehensive framework for assessing reporting quality. We therefore evaluated and compared reporting completeness in conventional versus machine-learning models. Methods Studies reporting the development, and internal and external validation of prognostic prediction models for COVID-19 using either conventional or machine learning-based algorithms were included. Literature searches were conducted in MEDLINE, Epistemonikos.org, and Scopus (up to July 31, 2024). Studies using conventional statistical methods were evaluated under TRIPOD, while machine learning-based studies were assessed using TRIPOD + AI. Data extraction followed TRIPOD and TRIPOD + AI checklists, measuring adherence per article and per checklist item. The protocol was prospectively registered at the Open Science Framework ( https://osf.io/kg9yw ). Results A total of 53 studies describing 71 prognostic models were identified. Overall, adherence to both guidelines was low, with significantly poorer compliance among machine learning-based studies (TRIPOD + AI) compared to conventional model studies (TRIPOD) (28.4% vs. 38.1%, 95% CI of difference: 4.1–15.4). No study fully adhered to abstract reporting requirements, and appropriate titles were included in only a minority of cases (29.0%, 95% CI: 16.1–46.6 for TRIPOD; 13.6%, 95% CI: 4.8–33.3 for TRIPOD + AI). Sample size calculations were not fully reported in any study. Reporting of methods and results sections was poor across both frameworks. Conclusion Lower adherence among machine learning studies reflects the relatively recent publication of the TRIPOD + AI guidelines (April 2024), which postdate many of the included studies. Both conventional and machine learning-based prediction models showed insufficient reporting, with major gaps in model description and performance reporting. Greater compliance with reporting guidelines is critical to improving the clarity, reproducibility, and clinical value of prediction model research.

Renewable energies are the future, and offshore wind is undoubtedly one of the renewable energy sources for the future. Foundations of offshore wind turbines are essential for its right development. There are several types: monopiles, gravity-based structures, jackets, tripods, floating support, etc., being the first ones that are most used up to now. This manuscript begins with a review of the offshore wind power installed around the world and the exposition of the different types of foundations in the industry. For that, a database has been created, and all the data are being processed to be exposed in clear graphic summarizing the current use of the different foundation types, considering mainly distance to the coast and water depth. Later, the paper includes an analysis of the evolution and parameters of the design of monopiles, including wind turbine and monopile characteristics. Some monomials are considered in this specific analysis and also the soil type. So, a general view of the current state of monopile foundations is achieved, based on a database with the offshore wind farms in operation.

Summary Tip‐scanning high‐speed atomic force microscopes (HS‐AFMs) have several advantages over their sample‐scanning counterparts. Firstly, they can be used on samples of almost arbitrary size since the high imaging bandwidth of the system is immune to the added mass of the sample and its holder. Depending on their layouts, they also enable the use of several tip‐scanning HS‐AFMs in combination. However, the need for tracking the cantilever with the readout laser makes designing tip‐scanning HS‐AFMs difficult. This often results in a reduced resonance frequency of the HS‐AFM scanner, or a complex and large set of precision flexures. Here, we present a compact, simple HS‐AFM designed for integrating the self‐sensing cantilever into the tip‐scanning configuration, so that the difficulty of tracking small cantilever by laser beam is avoided. The position of cantilever is placed to the end of whole structure, hence making the optical viewing of the cantilever possible. As the core component of proposed system, a high bandwidth tripod scanner is designed, with a scan size of 5.8 µm × 5.8 µm and a vertical travel range of 5.9 µm. The hysteresis of the piezoactuators in X‐ and Y‐axes are linearized using input shaping technique. To reduce in‐plane crosstalk and vibration‐related dynamics, we implement both filters and compensators on a field programmable analog array. Based on these, images with 512 × 256 pixels are successfully obtained at scan rates up to 1024 lines/s, corresponding to a 4 mm/stip velocity. SCANNING 38:889–900, 2016. © 2016 Wiley Periodicals, Inc.

Background As complete reporting is essential to judge the validity and applicability of multivariable prediction models, a guideline for the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) was introduced. We assessed the completeness of reporting of prediction model studies published just before the introduction of the TRIPOD statement, to refine and tailor its implementation strategy. Methods Within each of 37 clinical domains, 10 journals with the highest journal impact factor were selected. A PubMed search was performed to identify prediction model studies published before the launch of TRIPOD in these journals (May 2014). Eligible publications reported on the development or external validation of a multivariable prediction model (either diagnostic or prognostic) or on the incremental value of adding a predictor to an existing model. Results We included 146 publications (84% prognostic), from which we assessed 170 models: 73 (43%) on model development, 43 (25%) on external validation, 33 (19%) on incremental value, and 21 (12%) on combined development and external validation of the same model. Overall, publications adhered to a median of 44% (25th–75th percentile 35–52%) of TRIPOD items, with 44% (35–53%) for prognostic and 41% (34–48%) for diagnostic models. TRIPOD items that were completely reported for less than 25% of the models concerned abstract (2%), title (5%), blinding of predictor assessment (6%), comparison of development and validation data (11%), model updating (14%), model performance (14%), model specification (17%), characteristics of participants (21%), model performance measures (methods) (21%), and model-building procedures (24%). Most often reported were TRIPOD items regarding overall interpretation (96%), source of data (95%), and risk groups (90%). Conclusions More than half of the items considered essential for transparent reporting were not fully addressed in publications of multivariable prediction model studies. Essential information for using a model in individual risk prediction, i.e. model specifications and model performance, was incomplete for more than 80% of the models. Items that require improved reporting are title, abstract, and model-building procedures, as they are crucial for identification and external validation of prediction models.

To promote uniformity in measuring adherence to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, a reporting guideline for diagnostic and prognostic prediction model studies, and thereby facilitate comparability of future studies assessing its impact, we transformed the original 22 TRIPOD items into an adherence assessment form and defined adherence scoring rules. TRIPOD specific challenges encountered were the existence of different types of prediction model studies and possible combinations of these within publications. More general issues included dealing with multiple reporting elements, reference to information in another publication, and non-applicability of items. We recommend our adherence assessment form to be used by anyone (eg, researchers, reviewers, editors) evaluating adherence to TRIPOD, to make these assessments comparable. In general, when developing a form to assess adherence to a reporting guideline, we recommend formulating specific adherence elements (if needed multiple per reporting guideline item) using unambiguous wording and the consideration of issues of applicability in advance.