Assessing the quality of reports of randomized trials in pediatric complementary and alternative medicine
Bibliographic record
Abstract
OBJECTIVE: To evaluate the quality of reports of complementary and alternative medicine (CAM) randomized controlled trials (RCTs) in the pediatric population. We also examined whether there was a change in the quality of reporting over time. METHODS: We used a systematic sample of 251 reports of RCTs that used a CAM intervention. The quality of each report was assessed using the number of CONSORT checklist items included, the frequency of unclear allocation concealment, and a 5-point quality assessment instrument. RESULTS: Nearly half (40%) of the CONSORT checklist items were included in the reports, with an increase in the number of items included. The majority (81.3%) of RCTs reported unclear allocation concealment with no significant change over time. The quality of reports achieved approximately 40% of their maximum possible total score as assessed with the Jadad scale with no change over time. Information regarding adverse events was reported in less than one quarter of the RCTs (22%) and information regarding costs was mentioned in only a minority of reports (4%). CONCLUSIONS: RCTs are an important tool for evidence based health care decisions. If these studies are to be relevant in the evaluation of CAM interventions it is important that they are conducted and reported with the highest possible standards. There is a need to redouble efforts to ensure that children and their families are participating in RCTs that are conducted and reported with minimal bias. Such studies will increase their usefulness to a board spectrum of interested stakeholders.
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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 | 0.740 | 0.893 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.026 |
| Bibliometrics | 0.026 | 0.022 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".