Patient related outcome measures for breast augmentation mammoplasty: a systematic review
Bibliographic record
Abstract
We review the current literature for validated patient related outcome measures (PROM) in breast augmentation mammoplasty (BAM). Using Medline search between 1966 to 2018, using the search strategy ("patient reported outcome measure" OR "surveys or questionnaires") AND "breast" AND ("augment" OR "implant") was performed. A manual search with Google Scholar using the search term "Patient Reported Outcome Measures in Bilateral Augmentation Mammaplasty" was also performed. Once the search yielded its results, a further search of bibliographic references within the articles was also performed. The Medline computer search produced 72 results, with a Google Scholar search yielding two results and a bibliographic search of all articles revealing a further single result. Ten studies were included as they used validated PROM. Three articles used the same PROM (Breast-Q) and seven used different PROM, therefore 8 validated PROM were discovered. Bilateral augmentation mammoplasty has been demonstrated to confer an increase in patient reported outcomes in domains of satisfaction with breasts and psychological well-being. There is some decrease in physical well-being following this procedure. Validated PROMs provide objective data relating to different aspects of BAM. Combined with traditional surgeon-based outcome measures and implant registry data, they may provide a more comprehensive insight into the patient journey.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".