Decision Regret following Breast Reconstruction
Bibliographic record
Abstract
BACKGROUND: The relationship between satisfaction with information and decision regret has not been previously studied in breast reconstruction patients. The objective of this study, therefore, was to assess this relationship and the factors that may influence satisfaction with preoperative information, including self-efficacy (confidence with seeking medical knowledge). METHODS: All patients who underwent breast reconstruction between January of 2009 and March of 2011 were approached to complete the Modified Stanford Self-Efficacy Scale (1 to 10), the satisfaction with information subscale of the BREAST-Q (1 to 100), and the Decision Regret Scale (1 to 100). Two multinomial logistic regression models were built to assess the relationship between patient-reported satisfaction with information and decision regret, and to evaluate the relationship among satisfaction with information, self-efficacy level, and sociodemographic characteristics. RESULTS: In 100 participants (71 percent response rate), the mean Decision Regret Scale score was 9.3±17.3 of 100, and the majority of patients experienced no regret (60 percent). We found that regret was significantly reduced when patients were more satisfied with the preoperative information that they received from their plastic surgeons (β=0.95; 95 percent CI, 0.93 to 0.96). Furthermore, patients reported higher satisfaction with information when they possessed more self-efficacy irrespective of their sociodemographic characteristics (β=1.06; 95 percent CI, 1.04 to 1.09). CONCLUSIONS: Patients who possess lower levels of self-efficacy are at greater risk for experiencing dissatisfaction with the information that they receive in the preoperative period, and ultimately suffered more regret over their decision to undergo breast reconstruction. CLINICAL QUESTION/LEVEL OF EVIDENCE: Risk, IV.
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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 | 0.023 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".