Evaluating Meta-analyses in the General Surgical Literature
Bibliographic record
Abstract
OBJECTIVE: To assess the methodologic quality of meta-analyses of general surgery topics published in peer-reviewed journals. SUMMARY BACKGROUND DATA: Systematic reviews and meta-analysis are used to seek, summarize, and interpret primary studies on a given topic. Accordingly, systematic reviews and meta-analyses of high-quality primary studies may be the highest level of evidence for issues of prevention and treatment in evidence-based medicine. However, not all published meta-analyses are rigorously performed. METHODS: We searched MEDLINE (from January 1, 1997, to September 1, 2002) and reference lists and solicited general surgery specialists to identify relevant meta-analyses. Inclusion criteria were use of meta-analytic methods to pool the results of primary studies in general surgery on issues of diagnosis, causation, prognosis, or treatment. Our search strategies identified 487 potentially relevant articles. After excluding articles based on a priori criteria, 51 meta-analyses fulfilled eligibility criteria. In duplicate and independently, 2 reviewers assessed the quality of these meta-analyses using a 10-item index called the Overview Quality Assessment Questionnaire. RESULTS: Overall concordance between 2 independent reviewers was good (interobserver agreement 81%, and a kappa of 0.62 (95% CI 0.55-0.69). Of 51 relevant articles, 38 were published in surgical journals. Most studies had major methodologic flaws (median score of 3.3, scale of 1-7). Factors associated with low overall scientific quality included the absence of any prior meta-analyses publications by authors and meta-analyses produced by surgical department members without external collaboration. CONCLUSIONS: This critical appraisal of meta-analyses published in the general surgery literature demonstrates frequent methodologic flaws. The quality of these reports limits the validity of the findings and the inferences that can be made about the primary studies reviewed. To improve the quality of future meta-analyses, we recommend following guidelines for the optimal conduct and reporting of meta-analyses in general surgery.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | MetaresearchMeta-epidemiology (broad) Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| grok | MetaresearchMeta-epidemiology (broad) Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| opus | MetaresearchMeta-epidemiology (broad) Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.358 | 0.630 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.024 | 0.052 |
| Bibliometrics | 0.028 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".