The Impact of Delaying Breast Reconstruction on Patient Expectations and Health-Related Quality of Life: An Analysis Using the BREAST-Q
Bibliographic record
Abstract
Purpose: An understanding of patient expectations predicts better health outcomes following breast reconstruction. No study to date has examined how patient expectations for breast reconstruction and preoperative health-related quality of life vary with time since breast cancer diagnosis. Methods: Women consulting for breast reconstruction to a single surgeon’s practice over a 13-month period were enrolled in this cross-sectional study. Patients were asked to prospectively complete the BREAST-Q expectations and preoperative reconstruction modules. A retrospective chart review was then performed on eligible patients, and patient demographics, cancer-related factors, and comorbidities were collected. BREAST-Q scores were transformed using the equivalent Rasch method. Multivariate linear regression models were constructed to assess the association between BREAST-Q scores and time since cancer diagnosis. Results: Sixty-five patients met inclusion criteria for analysis and are characterized by a mean age of 53 ± 11 (34-79) years and a mean body mass index of 28 ± 6 (19-49). Most patients were treated by mastectomy (58%) or lumpectomy (23%). At the time of retrospective chart review, 29 (43%) patients had undergone reconstruction, most of which were delayed (59%). The mean latency from cancer diagnosis to reconstruction was 685 ± 867 days (range: 28-3322 days). Latency from cancer diagnosis to reconstruction was associated with a greater expectation of pain (β = 0.5; standard error [SE] = 0.005; 95% confidence interval [CI]: 0.003-0.027; P < .05), and a slower expectation for recovery (β = −0.5; SE = 0.004; 95% CI: −0.021 to −0.001; P < .05) after breast reconstruction. Latency from cancer diagnosis to reconstruction was associated with an increase in preoperative psychosocial well-being (β = 0.578; SE 0.009; 95% CI: 0.002-0.046; P < .05). Conclusion: Delaying breast reconstruction may negatively impact patient expectations of postoperative pain and recovery. Educational interventions aimed at understanding and managing patient expectations in the preoperative period may improve health-related quality of life and patient-related outcomes following initial breast cancer surgery.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".