Patient-reported satisfaction with reconstructed breasts in the long-term survivorship period: Comparison of autologous and nonautologous breast reconstruction.
Bibliographic record
Abstract
9043 Background: Breast cancer patients undergoing mastectomy may choose to have reconstruction performed using either their own tissue or an implant. As many patients are candidates for both, valid and reliable patient-centered outcomes data are crucial to shared medical decision-making. The objective of this study is to determine whether patient-reported satisfaction with their reconstructed breasts is dependent on type of reconstructive surgery and length of time from reconstruction. Methods: Participants were recruited from Memorial Sloan-Kettering Cancer, NY and the University of British Columbia, Canada. Patients completed the BREAST-Q, a new patient-reported outcome measure for breast surgery patients. The dependent variable was the BREAST-Q Satisfaction with Breast score, a 16-item scale scored from 0-100. Procedure type, time since surgery, and patient characteristics were independent variables. Univariate analysis and clinical judgment were used to identify variables included in the model, and multivariate linear regression models were constructed to control for confounders. Results: The study sample consisted of 510 women (response rate 62%). The sample was on average aged 54.3 ± 9.3 (range 21-81), surveyed 3.71 years ± 1.55 (range 1-9) after surgery, 66% were reconstructed using an implant. Type of surgery and laterality were found to be variables that predicted higher patient satisfaction with their breasts after controlling for radiation therapy, follow-up time, timing of surgery, age, body mass index, and major complications (surgery type p<0.001; laterality p<0.001, R-square=0.17). Conclusions: As there is a growing population of breast cancer survivors, understanding how a woman’s satisfaction with her reconstructed breasts changes over time is essential. This study suggests that patient satisfaction with breast reconstruction depends on the type of reconstruction a woman undergoes. This patient-centered outcome data can be used to enhance shared medical decision-making by providing patients with information about realistic expectations for satisfaction with breasts related to type of surgery chosen.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".