Clinical machine learning (ML) technologies can sometimes be biased and their use could exacerbate health disparities. The extent to which bias is present, the groups who most frequently experience bias, and the mechanism through which bias is introduced in clinical ML applications is not well described. The objective of this study was to examine instances of bias in clinical ML models. We identified the sociodemographic subgroups (using the PROGRESS-Plus framework) that experienced bias and the reported mechanisms of bias introduction METHODS: We searched MEDLINE, EMBASE, PsycINFO and Web of Science for all studies that evaluated bias on sociodemographic factors within ML algorithms created for the purpose of facilitating clinical care. The scoping review was conducted according to the JBI guide and reported using the PRISMA extension for scoping reviews.

Registry-based Randomized Controlled Trials (RRCTs) can provide internally valid results in a real-world context at relatively low effort and cost. However, the main characteristics, the extent to which the registry is utilized (e.g., proportion of data from registry), and registry-related limitations are not well characterized. This methodological review of RRCTs aims to analyze the trial design features, investigate potential usage options, and identify possible limitations of using registry data for RCTs.

In February 2023, the Journal of Clinical Epidemiology published 'The Problems with Systematic Reviews: A living Systematic Review.' In updating this living review for the first time to incorporate literature from May 2022 to May 2023, a new problem and several themes have emerged from 152 newly included articles relating to research culture This brings the total number of relevant articles up to 637 and the total number of problems with systematic reviews up to 68. This update documents a new problem: the lack of gender diversity of systematic review author teams. It also reveals emerging themes such as: fast science from systematic reviews on COVID-19; the failure of citation of methodological or reporting guidelines to predict high-quality methodological or reporting quality; and the influence of vested interests on systematic review conclusions. These findings coupled with a proliferation of research waste from "me-too" meta-research articles highlighting well-established problems in systematic reviews underscores the need for reforms in research culture to address the incentives for producing and publishing research papers. This update reports where the identified flaws in systematic reviews affect their conclusions drawing on 77 meta-epidemiological studies from the total 637 included articles. These meta-meta-analytic studies begin the important work of examining which problems threaten the reliability and validity of treatment effects or conclusions derived from systematic reviews. We recommend that meta-research endeavours evolve from merely documenting well-established issues to understanding lesser-known problems or consequences to systematic reviews.

Multiple tools exist for assessing the methodological quality of diagnosis and prognosis research. It can be challenging to decide on when to use which tool. We aimed to provide an overview of existing methodological quality assessment (QA) tools for diagnosis and prognosis studies, highlight the overlap and differences among these tools, and to provide guidance for choosing the appropriate tool.

Mortality prediction models are promising tools for guiding clinical decision-making and resource allocation in intensive care units (ICUs). Clearly specified predictor and outcome variables are necessary to enable external validation and safe clinical application of prediction models. The objective of this study was to identify the predictor and outcome variables used in different mortality prediction models in the ICU and investigate their reporting.

Small-for-gestational age (SGA) is a causal factor for malnutrition (undernutrition). The available evidence on this causal relationship is based on observational studies and suffers from confounding and collider biases. This study aimed to construct a theoretical causal model to estimate the effect of SGA on malnutrition in children under five years of age.

Many negative randomized controlled trials (RCTs) report spin in their conclusions to highlight the benefits of the experimental arm, which could correspond to a non-inferiority (NI) objective. We aimed to evaluate whether some negative superiority RCTs comparing two active interventions could correspond to an NI situation and to explore associated trial characteristics.

To evaluate real-time (day-to-day) adaptation of randomized controlled clinical trials (RCTs) with delayed endpoints - a "forward-looking optimal-experimentation" form of response-adaptive randomization (RAR). To identify the implied tradeoffs between lowered mortality, confidence intervals, statistical power, potential arm misidentification, and endpoint-rate change during the trial.

To identify, critically appraise and evaluate the performance measures of the available prediction models for outcomes in people with low back pain (LBP) receiving conservative treatment.

Randomized clinical trials (RCT) provide the most reliable estimates of treatment effectiveness for therapeutic interventions. However, flaws in their design and conduct may bias treatment effect estimates, leading to over or underestimation of the true intervention effect. This is especially relevant for complex interventions, such as those in rehabilitation, which are multifaceted and tailored for individual patients or providers, leading to variations in delivery and treatment effects.

With greater availability of participant data and biobank repositories following clinical trial completion, adequately describing future data and biological sample re-use plans to trial participants is increasingly important. We evaluated how trial teams currently describe current and future use of participant data and biological samples in participant information leaflets (PILs).

Programmed-death-1/ligand-1 inhibitors (PD-1/L1i's) have emerged as pivotal treatments for many cancers. A notable feature of this class of medicines is the dichotomous response pattern: A small (but clinically-relevant) percentage of patients (5% - 20%) benefit from deep and durable responses resembling functional cures (durable responders), while most patients experience only a modest or negligible response. Accurately predicting durable responders remains elusive due to the lack of a reliable biomarker. Another notable feature of these medicines is that different PD-1/L1's have obtained statistically significant results, leading to marketing approval, for some cancer indications, but not for others, with no discernible pattern. These puzzling inconsistencies have generated extensive discussions among oncologists. Proposed (but not entirely convincing) explanations include true underlying differences in efficacy for some types of cancer, but not others; or subtle differences in trial design.

In research evaluating statistical analysis methods, a common aim is to compare point estimates and confidence intervals (CIs) calculated from different analyses. This can be challenging when the outcomes (and their scale ranges) differ across datasets. We therefore developed a graphical method, the 'Banksia plot', to facilitate pairwise comparisons of different statistical analysis methods by plotting and comparing point estimates and confidence intervals from each analysis method both within and across datasets.

Health state utility (HSU) instruments for calculating quality-adjusted life years, such as the EORTC QLU-C10D, the PROMIS Preference Score (PROPr) and the EQ-5D-5L, yield different HSU values due to different modelling and different underlying descriptive scales. E.g. the QLU-C10D includes cancer-relevant dimensions such as nausea. This study aimed to investigate how these differences in descriptive scales contribute to differences in HSU scores by comparing scores of cancer patients receiving chemotherapy to those of the general population.

To systematically evaluate definitions of "racial health equity" and related terms within health-related academic literature.

To systematically review the comparative statistical performance (discrimination and /or calibration) of prognostic clinical prediction models (CPMs) and clinician judgment (CJ).