Outcomes and Outcome Measures in Breast Reduction Mammaplasty: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Reduction mammaplasty remains critical to the treatment of breast hypertrophy. No technique has been shown to be superior; however, comparison between studies is difficult due to variation in outcome reporting. OBJECTIVES: The authors sought to identify a comprehensive list of outcomes and outcome measures in reduction mammaplasty. METHODS: A comprehensive computerized search was performed. Included studies were randomized or nonrandomized controlled trials involving at least 100 cases of female breast hypertrophy and patients of all ages who underwent 1 or more defined reduction mammaplasty technique. Outcomes and outcome measures were extracted and tabulated. RESULTS: A total 106 articles were eligible for inclusion; 57 unique outcomes and 16 outcome measures were identified. Frequency of patient-reported and author-reported outcomes were 44% and 88%, respectively. Postoperative complications were the most frequently reported outcome (82.2%). Quality-of-life outcomes were accounted for in 37.7% of studies. Outcome measures were either condition-specific or generic; frequencies were as low as 1% and as high as 5.6%. Five scales were formally assessed in the breast reduction populations. Clinical measures were defined in 15.1% of studies. CONCLUSIONS: There is marked heterogeneity in reporting of outcomes and outcome measures in the literature. A standardized outcome set is needed to compare outcomes of various reduction mammaplasty techniques.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".