Optimizing Patient-Centered Care in Breast Reconstruction
Bibliographic record
Abstract
BACKGROUND: In breast reconstruction, achieving patient satisfaction is a central goal. While much is known about clinical variables that may influence satisfaction, little is known about how the process of care may affect patient perceptions of outcome. The aim of this study was to examine how preoperative information and interactions with the surgical and medical teams might influence patient satisfaction with the outcome. METHODS: A multicenter, cross-sectional study design was used. The BREAST-Q (breast reconstruction module) was administered in a postal survey to a cohort of breast reconstruction patients in North America. The association between patient satisfaction with the process of care and satisfaction with the outcome of breast reconstruction was evaluated using linear regression. Multivariate regression models were constructed to control for confounders and to identify predictors of outcome. RESULTS: The study sample (n=510; response rate, 66 percent) was characterized by a mean age of 54.3±9.3 years (range, 21.0 to 81.0 years) and a mean body mass index of 25.2±4.3 (range, 16.3 to 48.9). On multivariate analysis, satisfaction with information and satisfaction with the plastic surgeon predicted higher satisfaction with breasts (information, p<0.001; plastic surgeon, p=0.003; R(2)=0.29) and higher satisfaction with overall outcome (satisfaction with information, p<0.001; satisfaction with plastic surgeon, p<0.001; R(2)=0.31). CONCLUSIONS: Patient-centered care is an important aspect of quality of care. Patients' levels of satisfaction with preoperative information and their interaction with their plastic surgeon significantly influence satisfaction with their breasts and overall outcome. Future research to develop methods to enhance information delivery and the surgeon-patient relationship may optimize outcomes in breast reconstruction patients. CLINICAL QUESTION/LEVEL OF EVIDENCE: Risk, III.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".