The breast reconstruction satisfaction questionnaire (BRECON‐31): An affirmative analysis
Bibliographic record
Abstract
OBJECTIVE: To verify the subscale structure of the BRECON-31 using a test sample of women naïve to the questionnaire. METHODS: The BRECON-31 was administered to women following breast reconstruction. Their responses were subjected to principal components analysis (PCA) with a varimax rotation. Components were maintained with an Eigenvalue greater than one. Internal consistency reliability was measured with Cronbach's Alpha (CA). Components on the test pool analysis were then compared with the subscales developed on 128 women who completed the questionnaire during the development phase. RESULTS: Fifty women completed the BRECON-31. Development and test pools of women were similar across demographics, pathology, and surgical details, except the development sample was somewhat older (53 yo vs. 49 yo, P = 0.02). Using PCA, eight subscales again emerged: self-image, arm concerns, intimacy, satisfaction, recovery, self-consciousness, expectations, and breast appearance. A nipple, and abdominal strength and appearance subscales also emerged. Forty-one of the 45 items loaded similarly in the development and test pools. Internal consistency reliability was high, with CA in the test pool equaling or exceeding CA in the development pool in the majority of the subscales. CONCLUSIONS: The BRECON-31 factor structure identified in the development pool was supported by the test pool, with similar reliability.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".