Evaluation of meta‐analyses in the otolaryngological literature
Bibliographic record
Abstract
OBJECTIVE: To evaluate the quality of meta-analyses written on otolaryngological topics and define areas that can be improved upon in future studies. DATA SOURCES: MEDLINE (PubMed) and EMBASE databases were searched. The Cochrane database of systematic reviews was excluded, because these meta-analyses have already been critically evaluated and found to be of high quality. REVIEW METHODS: A systematic review of otolaryngological meta-analyses published between 1997 and 2006 (10 years) was performed in duplicate and independently by two authors. The search included 16 common otolaryngological terms. Inclusion criteria were meta-analytic methodology, otolaryngological topic, and at least one author from a department of otolaryngology. Fifty-one articles fulfilled eligibility criteria. In duplicate and independently, two reviewers assessed the quality of eligible meta-analyses using a validated 10-item index called the Overview Quality Assessment Questionnaire. Using the methods of Spearman, correlation coefficients are reported for associations examined between covariates and the Overall Score Quality. RESULTS: The majority of studies had methodologic flaws (mean score 3.9, scale of 1-7). Variables predicting higher-quality meta-analyses were publication in journals with higher impact factors (P = 0.0007) and authors who previously published meta-analyses (P = 0.0001). Using and reporting about a validity assessment tool needs to be improved upon in future studies. CONCLUSION: The quality of meta-analyses on otolaryngological topics is moderate. Future meta-analyses can be improved upon by following evidence-based guidelines for the reporting of meta-analyses, which include the use of a validity assessment tool, and consulting with an author familiar with meta-analysis methodology.
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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 | 0.292 | 0.518 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.029 | 0.078 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| 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".