Patient satisfaction with postmastectomy breast reconstruction
Bibliographic record
Abstract
BACKGROUND: At a time when the safety and effectiveness of breast implants remains under close scrutiny, it is important to provide reliable and valid evidence regarding patient outcomes. In the setting of postmastectomy reconstruction, patient satisfaction and quality of life may be the most significant outcome variables when evaluating surgical success. The objective of the current study was to identify predictors of patient satisfaction with breast appearance, including implant type, in a large sample of women who underwent breast reconstruction surgery using implants. METHODS: A multicenter, cross-sectional study design was used. A total of 672 women who had completed postmastectomy, implant-based reconstruction at 1 of 3 centers in North America were asked to complete the BREAST-Q (Reconstruction Module). Multivariate linear regression modeling was performed. RESULTS: Completed questionnaire data were available for 482 of the 672 patients. In 176 women, silicone implants were placed and in 306, saline implants were used. The multivariate model confirmed that patients' satisfaction with their breasts was significantly higher in patients with silicone implants (P = .016). The receipt of postmastectomy radiotherapy was found to have a significant, negative effect on breast satisfaction (P<.000) in both silicone and saline implant recipients. In addition, for women who received either silicone or saline implants, satisfaction diminished over time (P = .017). CONCLUSIONS: In the setting of postmastectomy reconstruction, patients who received silicone breast implants reported significantly higher satisfaction with the results of reconstruction than those who received saline implants. This information can be used to optimize shared medical decision-making by providing patients with realistic postoperative expectations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.005 | 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".