Uptake of core outcome sets in pediatric clinical trials: a protocol
Bibliographic record
Abstract
Abstract Introduction A growing number of pediatric 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, some methodology of COS development (especially patient involvement) can promote COS uptake. However, the uptake of COS in pediatric clinical trials needs to be further explored. The aim of this study is to provide information on the rationale and use of pediatric COS in clinical trials, and to analyze 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 pediatric COS identified in our previous systematic review and those subsequently included in the COMET database. We will extract the data including the target condition, population, intervention, list of core outcomes, and the details of patient involvement. Next, we will search Clinicaltrials.gov for trials on health conditions addressed by the identified COS. The comparability of the scopes in each COS-trial pair and for the outcomes in each clinical trial that are exact matches, general matches, and non-matches with outcomes in each relevant COS will be assessed. Finally, we will conduct a survey and semi-structured 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 Lanzhou University. Strengths and limitations of the proposed study The uptake of pediatric COS will be presented and analyzed in a comprehensive manner through comparative analysis of the literature and a combination of quantitative and qualitative methods. There will be language restrictions in the selection of the studies, and the survey and interview sample will include only subjects speaking English or Chinese. Both restrictions may limit the generalizability of our results.
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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 | 0.026 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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; a candidate call from one teacher head, not a consensus.
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".