The Impact of Psychiatric Diagnoses on Patient-reported Satisfaction and Quality of Life in Postmastectomy Breast Reconstruction
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to determine the prevalence of psychiatric diagnoses among a sample of breast reconstruction patients and measure the association between these diagnoses and reconstruction-related, patient-reported outcomes. BACKGROUND: The impact of psychiatric disorders in conjunction with breast cancer diagnosis, treatment, and reconstruction have the potential to cause significant patient distress but remains not well understood. METHODS: A retrospective review of postmastectomy breast reconstruction patients from 2007 to 2018 at Memorial Sloan Kettering Cancer Center was conducted. Patient demographics, comorbidities, cancer characteristics, psychiatric diagnoses, and BREAST-Q Reconstruction Module scores (measuring satisfaction with breast, well-being of the chest, psychosocial, and sexual well-being) at postoperative years 1 to 3 were examined. Mixed-effects models and cross-sectional linear regressions were conducted to measure the effect of psychiatric diagnostic class type and number on scores. RESULTS: Of 7414 total patients, 50.1% had at least 1 psychiatric diagnosis. Patients with any psychiatric diagnoses before reconstruction had significantly lower BREAST-Q scores for all domains at all time points. Anxiety (50%) and depression (27.6%) disorders were the most prevalent and had the greatest impact on BREAST-Q scores. Patients with a greater number of psychiatric diagnostic classes had significantly worse patient-reported outcomes compared with patients with no psychiatric diagnosis. Psychosocial (β: -7.29; 95% confidence interval: -8.67, -5.91), and sexual well-being (β: -7.99; 95% confidence interval: -9.57, -6.40) were most sensitive to the impact of psychiatric diagnoses. CONCLUSIONS: Mental health status is associated with psychosocial and sexual well-being after breast reconstruction surgery as measured with the BREAST-Q. Future research will need to determine what interventions (eg, screening, early referral) can help improve outcomes for breast cancer patients with psychiatric disorders undergoing breast reconstruction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".