Examining the Evidence in Anesthesia Literature: A Critical Appraisal of Systematic Reviews
Bibliographic record
Abstract
UNLABELLED: Systematic reviews are structured reviews that use scientific strategies to reduce bias in the collection, appraisal, and interpretation of relevant studies. We undertook a systematic review of published systematic reviews in perioperative medicine to summarize the areas currently covered by this type of literature, to evaluate the quality of systematic reviews in this field, and to assess some of the methodologic and reporting issues that are unique to systematic reviews. Computerized bibliographic databases, citation review, and hand searches were performed to identify eligible articles. Quality was assessed using the Overview Quality Assessment Questionnaire. Eight-two systematic reviews were found. Reviews in perioperative medicine tended to evaluate prophylactic or therapeutic interventions. No differences were seen in quality between reviews published in anesthesia and nonanesthesia journals. Nearly half of all systematic reviews had only minor or minimal flaws; however, methods can be improved with expanded search strategies, use of least two reviewers to assess each study, use of validated methods to evaluate quality, and assessment of potential sources of bias. IMPLICATIONS: The quality of systematic reviews relating to perioperative medicine was examined systematically and found to be similar in quality to those in other specialties. Adoption of recently published criteria on writing and methods could further improve this type of literature.
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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 | 0.299 | 0.607 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.010 |
| Bibliometrics | 0.043 | 0.027 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".