A tool for assessing the comprehensiveness of outcome reporting within clinical trials in pregnancy
Bibliographic record
Abstract
Abstract Introduction Clinical trials provide fundamental evidence used to inform healthcare decisions at patient‐ and population levels and it is thus important that trials consider outcomes relevant to both patients and stakeholders. Although validated tools assessing other aspects of trial integrity exist, there is no tool for assessing the breadth and completeness of outcomes measured. Our objective was to develop a comprehensiveness of outcome reporting (COR) tool to assess this within trials in pregnancy. Material and methods We developed a tool that aids in visualizing outcome reporting through the automatic generation of a heatmap, enabling assessment of the range of maternal and fetal/neonatal outcomes included in clinical trials. Outcome reporting and measurement of each study is compared to a context‐specific, user‐determined, ideal standard set of outcomes, created by initially considering all domains within five core outcome areas. These include mortality, morbidity, functioning/life‐impact, resource‐use, and adverse events, as identified by the most recent taxonomy for outcomes in medical research. We tested the tool’s functionality using trials previously identified as studies on obesity in pregnant patients, and further compared the utility of the COR Tool against Cochrane’s Risk of Bias 2.0 Tool using correlational analysis. Results The pilot heatmap using clinical trials studying obesity in pregnancy (n = 15), illustrated a lack of comprehensiveness of reported outcomes in the majority of studies. Included trials were found to readily report physiological/clinical outcome but consistently neglected outcome areas related to functioning, delivery of care, resource‐use, and adverse events. Outcome areas reported and measured were done so with largely varying degrees of quality. When the COR Tool was compared with Cochrane’s RoB 2.0 Tool on a scatter plot, only a weak correlation was found ( R = 0.2936, R 2 = 0.0862) Conclusions The COR Tool will promote transparency in clarifying what outcomes a trial’s conclusions are based on, encourage trialists to consider outcomes related to all aspects of maternal and fetal/neonatal health, and support reviewers in appraising outcome reporting and measurement in the assessment of trial integrity. Used in tandem with RoB tools and core outcome sets, we hope the COR Tool will meaningfully contribute to improving maternal‐infant health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.560 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".