A systematic review of patient reported outcome measures for women with macromastia who have undergone breast reduction surgery
Bibliographic record
Abstract
Patient satisfaction and outcomes following reduction mammoplasty is important to measure, being a being a reconstructive procedure with physical and cosmetic benefits. This study aimed to evaluate patient satisfaction and the various questionnaires that have been devised for this measurement. A systematic search of literature was performed in PubMed, Cochrane Library, Medline and Scopus databases from 1966 to July 2018 according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After application of pre-determined inclusion criteria by two authors, 95 articles were included. Data was extracted from included studies relating to demographics, surgical technique, questionnaires used and physical, psychological and aesthetic outcomes. Of the 95 studies included (9,716 patients), 58 studies (5,867 patients) reported on overall satisfaction with a mean rate of 90.26%. Researchers' own non-validated questionnaire was most commonly used in 52.6% of studies. Validated questionnaires used were most commonly the SF-36 (25.3%), Rosenberg self-esteem scale (RSES) (9.5%) and BREAST-Q (8.4%). All showed improvement in physical and mental health. Our findings suggest that women who have undergone reduction mammoplasty for breast hypertrophy report postoperative satisfaction and improvement in quality of life. Of the validated questionnaires used, a combination of those assessing both mental, physical and psychosocial health as well as breast-specific surveys were most commonly used and may provide an accurate assessment of patient outcomes.
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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 | 0.014 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".