Measuring Quality of Life in Oncologic Breast Surgery: A Systematic Review of Patient-Reported Outcome Measures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it