Guidance for reporting outcomes in clinical trials: scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Patients, families and clinicians rely on published research to help inform treatment decisions. Without complete reporting of the outcomes studied, evidence-based clinical and policy decisions are limited and researchers cannot synthesise, replicate or build on existing research findings. To facilitate harmonised reporting of outcomes in published trial protocols and reports, the Instrument for reporting Planned Endpoints in Clinical Trials (InsPECT) is under development. As one of the initial steps in the development of InsPECT, a scoping review will identify and synthesise existing guidance on the reporting of trial outcomes. METHODS AND ANALYSIS: We will apply methods based on the Joanna Briggs Institute scoping review methods manual. Documents that provide explicit guidance on trial outcome reporting will be searched for using: (1) an electronic bibliographic database search; (2) a grey literature search; and (3) solicitation of colleagues for guidance documents using a snowballing approach. Reference list screening will be performed for included documents. Search results will be divided between two trained reviewers who will complete title and abstract screening, full-text screening and data charting. Captured trial outcome reporting guidance will be compared with candidate InsPECT items to support, refute or refine InsPECT content and to assess the need for the development of additional items. Data analysis will explore common features of guidance and use quantitative measures (eg, frequencies) to characterise guidance and its sources. ETHICS AND DISSEMINATION: A paper describing the review findings will be published in a peer-reviewed journal. The results will be used to inform the InsPECT development process, helping to ensure that InsPECT provides an evidence-based tool for standardising trial outcome reporting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.272 | 0.389 |
| Meta-epidemiology (narrow) | 0.007 | 0.009 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.021 | 0.027 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.018 | 0.016 |
| Insufficient payload (model declined to judge) | 0.140 | 0.069 |
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".