Uptake of core outcome sets in paediatric clinical trials: a protocol
Bibliographic record
Abstract
INTRODUCTION: A growing number of paediatric core outcome sets (COS) have been developed in the past 20 years. Previous studies have provided many useful insights into the uptake of COS. In addition to the awareness of COS among clinical trialists, the COS development process (especially patient participation) and the actions of the developers can promote COS uptake. However, the uptake of COS in paediatric clinical trials needs to be further explored. The aim of this study is to provide information on the rationale and use of paediatric COS in clinical trials, and to analyse in depth the awareness and views of COS developers and clinical trialists about the development and use of COS. METHODS AND ANALYSIS: We will include all paediatric COS identified in our previous systematic review and those subsequently included in the Core Outcome Measures in Effectiveness Trials (COMET) database. We will extract the target condition, population, intervention, list of core outcomes and the details of patient involvement. Next, we will search the Clinicaltrials.gov and WHO International Clinical Trials Registry Platform for trials on health conditions addressed by the identified COS. We will assess the comparability of the scopes in each COS-trial pair and determine for the outcomes in each clinical trial if they match exactly or generally, or if they do not match, with the outcomes of their respective COS. Finally, we will conduct a survey and semistructured interviews among COS developers and clinical trialists to examine their views. ETHICS AND DISSEMINATION: Ethical approval for the study has been granted by the ethics committee of the Institute of Health Data Science, Lanzhou University (No. HDS-202405-01). This study was registered on COMET (https://www.comet-initiative.org/Studies/Details/3122).
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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 | 0.280 | 0.296 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.073 | 0.024 |
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".