Meta-Analyses in Plastic Surgery: Can We Trust Their Results?
Bibliographic record
Abstract
BACKGROUND: Meta-analyses are common in the plastic surgery literature, but studies concerning their quality are lacking. The authors assessed the overall quality of meta-analyses in plastic surgery, and attempted to identify variables associated with scientific quality. METHODS: A systematic review of meta-analyses published in seven plastic surgery journals between 2007 and 2017 was undertaken. Publication descriptors and methodologic details were extracted. Articles were assessed using the following two instruments: A Measurement Tool to Assess Systematic Reviews (AMSTAR) and AMSTAR 2. RESULTS: Seventy-four studies were included. The number of meta-analyses per year increased. Most meta-analyses assessed a single intervention (59.5 percent), and pooled a mean of 20.9 studies (range, two to 134), including a mean of 2463 patients (range, 44 to 14,884). Most meta-analyses were published in Plastic and Reconstructive Surgery (44.6 percent) and included midlevel evidence (II to IV) primary studies. Only 16.2 percent of meta-analyses included randomized controlled trials. Meta-analyses generally reported positive (81.1 percent) and significant results (77.0 percent). Median AMSTAR score was 7 of 11 (interquartile range, 5 to 8). Higher AMSTAR scores correlated with more recent meta-analyses that provided a rationale for statistical pooling, and appropriately managed methodologic heterogeneity (r = 0.66; p < 0.01). CONCLUSIONS: Despite an increase in number and quality, meta-analyses are at high risk of bias because of the low level of evidence of included primary studies and heterogeneity within and between primary studies. Plastic surgeons should be aware of the pitfalls of conducting and interpreting meta-analyses.
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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 | 0.421 | 0.749 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.025 | 0.035 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.012 | 0.006 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".