Evaluating Breast Reconstruction Reviews Using A Measurement Tool to Assess Systematic Reviews (AMSTAR)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it