Methodologic Quality of Systematic Reviews Published in the Plastic and Reconstructive Surgery Literature
Bibliographic record
Abstract
BACKGROUND: Well-conducted systematic reviews have a critical role in informing evidence-based decision-making in plastic surgery. The authors' objective was to assess the methodologic quality of systematic reviews in the plastic surgery literature. METHODS: The authors systematically assessed all systematic reviews in 10 high-impact plastic surgery journals using MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials, and the Cochrane Database of Systematic Reviews from 2003 to 2013. These were evaluated for methodologic quality using A Measurement Tool to Assess Systematic Reviews (AMSTAR), a validated 11-point instrument. RESULTS: After removal of duplicates and screening titles and abstracts, 190 systematic reviews met eligibility criteria. The majority of systematic reviews were published in Plastic and Reconstructive Surgery (n = 88). The most common domain covered was reconstruction (17.9 percent). Using AMSTAR, the median score was 4 (interquartile range, 2.25 to 6.00) on a scale of 1 to 11. No increase in AMSTAR score was observed with time (p = 0.18). Almost half of all systematic reviews (48.4 percent) included at least two independent data extractors, and less than one-third of them (15.3 percent) searched unpublished studies or provided a list of both included and excluded studies (14.8 percent). The methodologic quality of included primary studies was evaluated in 35.3 percent. CONCLUSIONS: Systematic reviews in plastic surgery demonstrated inadequate adherence to methodologic standards of quality, which raises concerns about validity. There has been an increase in the number of systematic reviews published in plastic surgery over the past decade, yet there has been no significant improvement observed in methodologic quality.
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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 | 0.630 | 0.832 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.016 |
| Bibliometrics | 0.066 | 0.053 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.005 | 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".