Assessing the Comprehensiveness of Outcome Reporting in Obstetric Trials: Development of a Reporting Tool
Bibliographic record
Abstract
Objective: Clinical trials provide fundamental evidence used to inform healthcare decisions at patient- and population levels. It is therefore important that trials evaluate outcomes considered relevant by patients and relevant stakeholders. Although validated tools assessing other aspects of trial integrity exist, there is no tool for assessing the breadth and completeness of outcomes being measured. We have developed the Comprehensiveness of Outcome Reporting (COR) Tool to assist systematic reviewers and trialists in evaluating and choosing trial outcomes within the dynamic context of obstetrics. Methods: We identified five core outcome areas – mortality, clinical/physiological, functioning/life-impact, resource-use, and adverse events – from a published taxonomy for outcomes in medical research, and programmed an excel-based tool capable of producing a heatmap to enable users to visualize whether trial outcomes appropriately represent all outcomes areas for both mother and fetus. We used a mock-heatmap to demonstrate the tool’s utility in assessing comprehensiveness of outcome reporting in obstetric trials. Results: This excel-based tool guides users through a series of simple questions regarding the clinical trial(s) being assessed, producing a heatmap output that depicts the spread of reported outcome areas. Trends are readily interpreted with a heatmap, with over/under-reported maternal and fetal-neonatal outcome areas clearly highlighted. Conclusions: Comprehensive reporting of outcomes is necessary to ensure that interventions truly result in improved outcomes in all core areas. The COR Tool will enable systematic reviewers and trialists to determine and select outcomes with more breadth and completeness, encouraging transparency and the drawing of valid clinical conclusions. Funding: None
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.582 | 0.782 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.027 | 0.021 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier 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".