Assessment of Usefulness of Randomized Control Trials in Child Health Research Published in 2007 and 2017
Bibliographic record
Abstract
OBJECTIVE: To examine how clinical usefulness in pediatric research with randomized controlled trials (RCTs) has changed over a 10-year period via a research usefulness tool composed of unique clinical usefulness criteria. STUDY DESIGN: We leveraged a pre-existing sample of child health RCTs published in 2007, used by our team in a previous study. Using the same methods, a research librarian executed a literature search in the Cochrane Central Register of Controlled Trials for the 2017 cohort. We included the first 300 eligible citations from the randomly ordered list for each year, creating two cohorts of 300 publications each, 1 in 2007 and 1 in 2017. Each publication was analyzed and data regarding primary and secondary outcomes, as well as 11 unique criteria of clinical usefulness, were extracted. Each publication was then graded using a tool created by our research team. After quality review, statistical analysis was then performed. RESULTS: Six hundred pediatric RCT publications were included in this review. The mean score increased from 6.07 in 2007 to 9.20 in 2017 (P < .001). Usefulness factors that saw the largest increase in reporting were context placement, funding statements, and conflict of interest statements, while patient centeredness, value for money, and raw data availability remained infrequently reported. CONCLUSION: Our results demonstrate that clinical usefulness of pediatric research improved over this 10-year period, but there are still areas that need a great deal of improvement in order to maximize clinical usefulness and reduce 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 | 0.591 | 0.876 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.026 | 0.071 |
| Bibliometrics | 0.021 | 0.014 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".