Assessing the quality of reports of systematic reviews in pediatric complementary and alternative medicine
Bibliographic record
Abstract
OBJECTIVE: To examine the quality of reports of complementary and alternative medicine (CAM) systematic reviews in the pediatric population. We also examined whether there were differences in the quality of reports of a subset of CAM reviews compared to reviews using conventional interventions. METHODS: We assessed the quality of reports of 47 CAM systematic reviews and 19 reviews evaluating a conventional intervention. The quality of each report was assessed using a validated 10-point scale. RESULTS: Authors were particularly good at reporting: eligibility criteria for including primary studies, combining the primary studies for quantitative analysis appropriately, and basing their conclusions on the data included in the review. Reviewers were weak in reporting: how they avoided bias in the selection of primary studies, and how they evaluated the validity of the primary studies. Overall the reports achieved 43% (median = 3) of their maximum possible total score. The overall quality of reporting was similar for CAM reviews and conventional therapy ones. CONCLUSIONS: Evidence based health care continues to make important contributions to the well being of children. To ensure the pediatric community can maximize the potential use of these interventions, it is important to ensure that systematic reviews are conducted and reported at the highest possible quality. Such reviews will be of benefit to a broad 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 | 0.676 | 0.899 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.018 |
| Bibliometrics | 0.051 | 0.039 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 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".