Reporting of interventions and “standard of care” control arms in pediatric clinical trials: a quantitative analysis
Bibliographic record
Abstract
BACKGROUND: In pediatric medicine, the usual treatment received by children ("standard of care") varies across centers. Evaluations of new treatments often compare to the existing "standard of care" to determine if a treatment is more effective, has a better safety profile, or costs less. The objective of our study was to evaluate intervention and "standard of care" control arms reported in published pediatric clinical trials. METHODS: Pediatric clinical trials, published in 2014, reporting the use of a "standard of care" control arm were included. Duplicate assessment of reporting completeness was done using the 12-item TIDieR (Template for Intervention Description and Replication) checklist for both the "standard of care" control arms and intervention arms within the same published study. RESULTS: Following screening, 214 pediatric trials in diverse therapeutic areas were included. Several different terms were used to describe "standard of care." There was a significant difference between the mean reported TIDieR checklist items of "standard of care" control arms (5.81 (SD 2.13) and intervention arms (8.45 (SD 1.39, p < 0.0001). CONCLUSIONS: Reporting of intervention and "standard of care" control arms in pediatric clinical trials should be improved as current "standard of care" reporting deficiencies limit reproducibility of research and may ultimately contribute to research waste.
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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 | 0.784 | 0.913 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".