Evaluating Breast Reconstruction Reviews Using A Measurement Tool to Assess Systematic Reviews (AMSTAR)
Bibliographic record
Abstract
Background: Breast reconstruction is an important aspect in breast cancer treatment. Methods: A comprehensive search of MEDLINE, Embase, and the Cochrane Library of Systematic Reviews was performed. Systematic reviews and meta-analyses that focused on breast reconstruction and were published between 2000 and 2020 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, reporting adherence to Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, number of citations, and number of studies included. Results: The average AMSTAR score was moderate (5.32). There was a significant increase in AMSTAR score (P < 0.01) and number of studies (P < 0.01) over time. There were no significant correlations between AMSTAR score and impact factor (P = 0.038), and AMSTAR score and number of citations (P = 0.52), but there was a significant association between AMSTAR score and number of studies (P = 0.013). Studies that adhered to the PRISMA statement had a higher AMSTAR score on average (P < 0.01). Conclusions: Systematic reviews and meta-analyses about breast reconstruction had, on average, a moderate AMSTAR score. The number of studies and methodological quality have increased over time. Study characteristics including adherence to PRISMA guidelines are associated with improved methodological quality. Further improvements in specific AMSTAR domains would improve the overall methodological 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 | 0.298 | 0.587 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.019 | 0.041 |
| Bibliometrics | 0.053 | 0.040 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".