Validation of a Questionnaire for Measuring Morbidity in Breast Hypertrophy
Bibliographic record
Abstract
BACKGROUND: There is a growing body of evidence suggesting that body mass index and predicted breast resection weight may not be appropriate criteria for determining insurance eligibility for breast reduction surgery. Eligibility should ideally be based on need. However, no method for determining need in patients seeking reduction surgery currently exists. The purpose of this investigation was to develop a validated questionnaire for measuring the burden of breast hypertrophy. METHODS: Forty-five symptoms specific to breast hypertrophy were incorporated into a questionnaire that was subsequently administered to a sample of 101 women. Reliability and validity testing was performed according to established psychometric criteria. RESULTS: Three items were omitted based on low item remainder coefficients (Cronbach's alpha) and three were eliminated because of excessive skew. Intraclass correlation coefficients of 0.85 indicated favorable test-retest reliability. Content validity was achieved through the study design and then confirmed by a group of 11 plastic surgeons. The questionnaire showed reasonable criterion validity when compared with corresponding domains in the Short Form-36. Construct validity was excellent. Exploratory factor analysis revealed five questionnaire subdomains: (1) physical implications, (2) poor self-concept, (3) body pain, (4) negative social interactions, and (5) physical appearance. CONCLUSIONS: The authors have developed an evaluative tool termed the Breast Reduction Assessed Severity Scale Questionnaire for measuring the burden of breast hypertrophy. The questionnaire produces subdomain scores and an overall measurement of the burden of breast hypertrophy that may be useful in the assessment of patients.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".