Measuring Quality of Life in Oncologic Breast Surgery: A Systematic Review of Patient-Reported Outcome Measures
Bibliographic record
Abstract
Multiple randomized trials demonstrate equivalent survival between BCT and mastectomy, but clinical outcomes research must also evaluate patient satisfaction and quality of life. This review analyzes existing patient-reported outcome (PRO) measures in oncologic breast surgery to assess utility and make recommendations for future research. We performed a systematic literature review to identify PRO measures used in oncologic breast surgery patients. After applying inclusion and exclusion criteria, qualifying instruments were assessed for adherence to international guidelines for health outcomes instrument development and validation. Ten measures underwent development and psychometric evaluation in an oncologic breast surgery population. Five of ten measures (EORTC QLQ BR-23, FACT-B, HBIS, BIBCQ, and BREAST-Q) reported an adequate development and validation process. Three of these 5 measures (EORTC QLQ BR-23, FACT-B, HBIS) focused on non-surgical treatment issues. A fourth instrument (BIBCQ) did not address aesthetic concerns after breast reconstruction. The fifth instrument (BREAST-Q) was developed for use in patients undergoing mastectomy ± reconstruction, but did not address breast-conserving therapy. Overall, two key limitations were noted: 1) surgery-specific issues of breast-conserving surgery patients were not well represented and 2) measures were largely developed without the aid of newer psychometric methods that may improve their clinical utility. Reliable and valid PRO measures in breast cancer patients exist, but even the best instruments do not address all important surgery-specific and psychometric issues of oncologic breast surgery patients. Newer psychometric methods would facilitate development of scales for use in individual patient care as well as group level comparisons.
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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 | 0.020 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".