Evaluating the Quality of Systematic Reviews and Meta-Analyses About Breast Augmentation Using AMSTAR
Bibliographic record
Abstract
Abstract Background Breast augmentation is one of the most commonly performed cosmetic surgeries worldwide. Therefore, it is imperative to have evidence with high methodological quality to guide clinical decision making. Objectives To evaluate the methodological quality of the systematic reviews (SRs) focused on breast augmentation. Methods A comprehensive search of MEDLINE, Embase, and the Cochrane Library of Systematic Reviews was performed. SRs that have a particular focus on breast augmentation and were published in the top 15 plastic and reconstructive surgery journals were included. Quality assessment was performed using a measurement tool to assess systematic reviews (AMSTAR). Study characteristics were extracted including journal and impact factor, year of publication, country affiliation of the corresponding author, reporting adherence to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, number of citations, and number of studies included. Results Among the 22 studies included for analysis, the mean AMSTAR score was moderate (5.55), with no SR achieving good quality (AMSTAR score of ≥9). There were no significant associations between AMSTAR score and journal impact factor, number of citations, year of publication, or number of included studies. Studies that reported adherence to PRISMA guidelines on average scored higher on the AMSTAR tool (P = 0.03). Conclusions The methodological quality of reviews about breast augmentation was found to be moderate, with no significant increase in studies or quality over time. Adherence to PRISMA guidelines and increased appraisal of SRs about breast augmentation using methodological assessment tools would further strengthen methodological quality and confidence in study findings.
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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 | 0.214 | 0.523 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.026 | 0.052 |
| Bibliometrics | 0.030 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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, 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".