Validation of the electronic version of the BREAST-Q in the army of women study
Bibliographic record
Abstract
Women undergoing surgery for primary breast cancer can choose between breast conserving therapy and mastectomy (with or without breast reconstruction). Patients often turn to outcomes data to help guide the decision-making process. The BREAST-Q is a validated breast surgery-specific patient-reported outcome measure that evaluates satisfaction, quality of life, and patient experience. It was originally developed for paper-and-pencil administration. However, the BREAST-Q has increasingly been administered electronically. Therefore, the aim of this study was to evaluate the psychometric properties of an electronic version of the BREAST-Q in a large online survey. Women with a history of breast cancer surgery recruited from the Love/AVON Army of Women program completed an electronic version of the BREAST-Q in addition to the Impact of Cancer Survey and PTSD Checklist. Traditional psychometric analyses were performed on the collected data. BREAST-Q data were collected from 6748 women (3497 Breast Conserving Therapy module, 1295 Mastectomy module, 1956 Breast Reconstruction module). Acceptability was supported by a high response rate (82%), low frequency of missing data (<5%), and maximum endorsement frequencies (<80%) in all but 17 items. Scale reliability was supported by high Cronbach's α coefficients (≥0.78) and item-total correlations (range of means, 0.65-0.91). Validity was supported by interscale correlations, convergent and divergent hypotheses as well as clinical hypotheses. The electronically administered BREAST-Q yields highly reliable, clinically meaningful data for use in clinical outcomes research. The BREAST-Q can be used in the clinical setting, whether administered electronically or using paper-and-pencil, at the choice of the patient and surgeon.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".