Transparent Reporting of Pediatric Clinical Trial Interventions: TIDieR-Children and Adolescents
Bibliographic record
Abstract
Pediatric randomized controlled trials (RCTs) inform decisions concerning the choice of interventions in children and adolescents. To enable the implementation of effective interventions, RCT reports need to provide adequate details on the elements, infrastructure, and delivery of these interventions. Using the 12-item Template for the Intervention Description and Replication (TIDieR) framework, an international team developed guidance for comprehensive reporting of trial interventions in pediatric RCT protocols and reports. We (1) identified initial pediatric considerations (PCs) and examples of good reporting using 50 recent pediatric RCT reports, (2) held an expert panel meeting, (3) conducted a Family Caregiver Workshop to discuss and get input on PCs, (4) compiled PCs and examples of good reporting, and (5) achieved consensus on final PCs and examples. Thirteen PCs reached consensus; they address how trial intervention materials were appropriate for the age and developmental stage of trial participants, which adjustments to enhance palatability of medications and acceptability of interventions were implemented, and how pediatric-specific dosing was determined. Consensus was also reached on accompanying good reporting exemplars. Presenting a minimum set of considerations pertinent to pediatric trial interventions, the TIDieR-Child & Adolescent Health (TIDieR-C) checklist can help trial authors and evidence end users comprehensively report and appraise tested interventions. It can be used with the pediatric-specific extensions of the Standard Protocol Items for Randomized Trials (SPIRIT) and Consolidated Standards of Reporting Trials (CONSORT): SPIRIT-Children & Adolescents and CONSORT-Children & Adolescents. Uptake of this guidance may lead to improved understanding, replicability, and implementation fidelity of effective trial interventions.
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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 | 0.747 | 0.872 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.007 | 0.014 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.018 | 0.012 |
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".