Re-examining content validity of the BREAST-Q more than a decade later to determine relevance and comprehensiveness
Bibliographic record
Abstract
PURPOSE: The BREAST-Q is the most used patient-reported outcome measure (PROM) in breast cancer surgery. The purposes of this study were to re-examine the content validity of BREAST-Q cancer modules (mastectomy, lumpectomy and reconstruction) and to determine the need for new scales. METHODS: Interviews were conducted with women with breast cancer (Stage 0-4, any treatment), and were audio-recorded and transcribed verbatim. Deductive (based on original BREAST-Q conceptual framework) and inductive (new codes from the data) content analysis approaches were used to analyze the data. The number of codes that mapped to BREAST-Q were recorded. RESULTS: Dataset included 3948 codes from 58 participants. Most of the breast (n = 659, 96%) and all psychosocial (n = 127, 100%), sexual (n = 179, 100%) and radiation-related (n = 79, 100%) codes mapped to BREAST-Q Satisfaction with Breast, Psychosocial Wellbeing, Sexual Wellbeing and Adverse Effects of Radiation scales, respectively. For the physical wellbeing codes (n = 939) for breast/chest and arm, 34% (n = 321) mapped to the Physical Wellbeing-Chest scale. Most of the abdomen codes (n = 311) mapped to Satisfaction with Abdomen (n = 90, 76%) and Physical Wellbeing-Abdomen (n = 171, 89%) scales. Codes that did not map (n = 697, 30%) covered breast sensation and lymphedema. Concerns related to fatigue, cancer worry, and work impact were most reported and did not map to BREAST-Q. CONCLUSION: The BREAST-Q, which was developed using extensive patient input more than a decade ago, is still relevant. To ensure the BREAST-Q remains comprehensive, new scales for upper extremity lymphedema, breast sensation, fatigue, cancer worry, and work impact were developed.
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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 | 0.159 | 0.493 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".