Scope for improvement in the quality of reporting of systematic reviews. From the Cochrane Musculoskeletal Group.
Bibliographic record
Abstract
OBJECTIVE: To assess the quality of reporting in Cochrane musculoskeletal systematic reviews (excluding back and injury reviews). METHODS: This study assessed all the Cochrane Musculoskeletal Group's systematic reviews from Issue 4, 2002, of the Cochrane Library Database of Systematic Reviews. Two reviewers independently extracted data and assessed quality. Two assessment tools were used, including an 18 item checklist and flow chart developed by the Quality of Reporting of Meta-analysis (QUOROM) consensus group, and a 10 item scale, the Oxman-Guyatt Overview Quality Assessment Questionnaire (OQAQ). One question on the latter scale (item 10) scores overall quality on a 7 point scale, with high scores indicating superior quality. Data were analyzed using univariate approaches. RESULTS: The 57 systematic reviews assessed were found to have good overall quality, with scores on individual items revealing only minor flaws. Documenting the flow of included and excluded studies and summarizing the results are 2 areas needing improvement in reporting. According to the Oxman-Guyatt scale the overall scientific quality of the Cochrane musculoskeletal reviews was good [mean 5.02 (95% CI 3.71-6.32)]. CONCLUSION: Our study found that the reporting quality of Cochrane musculoskeletal systematic reviews was generally good, although there was room for improvement. For example, it might be feasible to develop specific guidelines for reporting protocols. Certainly more work is needed in reporting search results, documentation of the flow of studies, identification of the type of studies, and summarization of the key findings.
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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 | 0.405 | 0.652 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.016 | 0.014 |
| Bibliometrics | 0.054 | 0.046 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.016 | 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".