Breast reconstructive surgery in medically underserved women with breast cancer
Bibliographic record
Abstract
BACKGROUND: Breast reconstructive surgery can improve mastectomy patients' emotional relationships and social functioning, but it may be underutilized in low-income, medically underserved women. This study assessed the impact of patient-physician communication on rates of breast reconstructive surgery in low-income breast cancer (BC) women receiving mastectomy. METHODS: A cross-sectional, California statewide survey was conducted of women with income less than 200% of the Federal Poverty Level and receiving BC treatment through the Medicaid Breast and Cervical Cancer Treatment Program. A subset of 327 women with nonmetastatic disease who underwent mastectomy was identified. Logistic regression was used for data analysis. The chief dependent variable was receipt of or planned breast reconstructive surgery by patient report at 6 months after diagnosis; chief independent variables were physician interactive information giving and patient perceived self-efficacy in interacting with physicians. RESULTS: Greater physician information giving about BC and its treatment and greater patient perceived self-efficacy positively predicted breast reconstructive surgery (OR=1.12, P=.04; OR=1.03, P=.01, respectively). The observed negative effects of language barriers and less acculturation among Latinas and lower education at the bivariate level were mitigated in multivariate modeling with the addition of the patient-physician communication and self-efficacy variables. CONCLUSIONS: Empowering aspects of patient-physician communication and self-efficacy may overcome the negative effects of language barriers and less acculturation for Latinas, as well as of lower education generally, on receipt of or planned breast reconstructive surgery among low-income women with BC. Intervening with these aspects of communication could result in breast reconstructive surgery rates more consistent with the general population and in improved quality of life among this disadvantaged group.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".