Development of a breast reconstruction satisfaction questionnaire (BRECON‐31): Principal components analysis and clinimetric properties
Bibliographic record
Abstract
BACKGROUND: A reliable, valid questionnaire is essential to assess patient satisfaction with breast reconstruction. METHODS: A 105-item pilot BRECON questionnaire was previously developed. One hundred eighty-one women with breast reconstruction were mailed the pilot BRECON, BREAST-Q, and EQ-5D questionnaires. Fifty women were re-mailed the BRECON. Based on the responses, the BRECON was refined using statistical means and principal components analysis (PCA). Reliability was assessed using the intraclass correlation coefficient (ICC) and Cronbach's alpha. Validity was assessed by comparing subscales of the BRECON to the BREAST-Q and comparing a summary score of the BRECON-31 to the EQ-5D using the Pearson's correlation coefficient (PCC). RESULTS: A total of 71% (128/181) of women completed the three questionnaires, and 86% (43/50) of women responded to the re-mailed BRECON. Statistical methods and PCA maintained 31 items covering eight components including self-image, arm concerns, intimacy, satisfaction, recovery, self-consciousness, expectations, and breast appearance. A 4-item "nipple" subscale and a 10-item "abdominal" subscale were developed for use where applicable. Measures of reliability and validity were high: Cronbach's alpha ranged from 0.67 to 0.91, ICC ranged from 0.55 to 0.85, and PCC ranged from 0.42 to 0.76. CONCLUSIONS: A reliable, valid 31-item breast reconstruction satisfaction questionnaire was 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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".