Patient-Reported Outcome Measures in Reconstructive Breast Surgery
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcomes provide an invaluable tool in the assessment of outcomes in plastic surgery. Traditionally, patient-reported outcomes have consisted of either generic or ad hoc measures; however, more recently, there has been interest in formally constructed and validated questionnaires that are specifically designed for a particular patient population. The purpose of this systematic review was to determine whether generic measures still have a role in the evaluation of breast reconstruction outcomes, given the recent popularity and push for use of specific measures. METHODS: A systematic review was performed to identify all articles using patient-reported outcomes in the assessment of postmastectomy breast reconstruction. Frequency of use was tabulated and the most frequently used tools were assessed for success of use, using criteria described previously by the Medical Outcomes Trust. RESULTS: To date, the most frequently used measures are still generic measures. The 36-Item Short-Form Health Survey was the most frequently used and most successfully applied showing evidence of responsiveness in multiple settings. Other measures such as the Hospital Anxiety and Depression Scale, the Hopwood Body Image Scale, and the Rosenberg Self-Esteem Scale were able to show responsiveness in certain settings but lacked evidence as universal tools for the assessment of outcomes in reconstructive breast surgery. CONCLUSIONS: Despite the recent advent of measures designed specifically to assess patient-reported outcomes in the breast reconstruction population, there still appears to be a role for the use of generic instruments. Many of these tools would benefit from undergoing formal validation in the breast reconstruction population.
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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 | 0.054 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".