Assessing the Quality of Reporting on Quality Improvement Initiatives in Breast Reconstruction: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: There has been a recent increase in the number and complexity of quality improvement (QI) studies in breast reconstruction. To assist with the development of thorough QI reporting practices, with the goal of improving the transferability of these initiatives, the authors conducted a systematic review of studies describing the implementation of QI initiatives in breast reconstruction. The authors used the Standards for Quality Improvement Reporting Excellence (SQUIRE) 2.0 guideline to appraise the quality of reporting of these initiatives. METHODS: English language articles published in Embase, MEDLINE, CINAHL, and the Cochrane databases were searched. Quantitative studies evaluating the implementation of QI initiatives in breast reconstruction were included. The primary endpoint of interest in this review was the distribution of studies according to SQUIRE 2.0 criteria scores in proportions. Abstracts and full-text screening, and data extraction were completed independently and in duplicate by the review team. RESULTS: The authors screened 1107 studies, of which 53 full texts were assessed and 35 met inclusion criteria. In our assessment, only three studies (9%) met all 18 SQUIRE 2.0 criteria. SQUIRE 2.0 criteria that were met most frequently were abstract, problem description, rationale, and analysis. The lowest SQUIRE 2.0 scores appeared in the interpretation criteria. CONCLUSIONS: Significant opportunity exists to improve QI reporting in breast reconstruction, especially in the realm of costs, strategic tradeoffs, ethical considerations, project sustainability, and potential for spread to other contexts. Improvements in these areas will help to further advance the transferability of QI initiatives in breast reconstruction.
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.
How this classification was reachedexpand
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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".