Higher Distress in Patients with Breast Cancer Is Associated with Declining Breast Reconstruction
Bibliographic record
Abstract
Background: Distress among newly diagnosed patients with breast cancer is common and may have an impact on their surgical decision-making. The revised Edmonton Symptom Assessment System (ESAS-r) is a validated instrument that provides an estimate of patients’ total distress, and no previous study has related preoperative scores to the choice to have breast reconstruction. Methods: Women with breast cancer treated at the Princess Margaret Cancer Centre in 2014 were reviewed, and patient and tumor characteristics were collected from local databases. Breast reconstruction status was obtained from patients’ electronic medical records until April 2017. A multivariable logistic regression model assessed for an independent association between preoperative ESAS-r total distress scores and patients’ decision to have breast reconstruction. Results: A total of 312 patients were analyzed. ESAS-r values had an overall median score of 10.0 and ranged from 0 to 69 (interquartile range, 17). Of these patients, 82 chose to undergo breast reconstruction surgery (26.8%). Multivariable logistic regression analysis showed that higher ESAS-r scores were associated with patients forgoing breast reconstruction surgery (lumpectomy-alone group: odds ratio estimate, 1.034 [1.004–1.064], P = 0.025; mastectomy-alone group: odds ratio estimate, 1.031 [1.004–1.059], P = 0.023). Conclusions: This study of patients with breast cancer found that higher distress scores as measured by the ESAS-r were associated with reduced breast reconstruction. Distress in patients with breast cancer is important to address, as it is often treatable, and its resolution may unmask a desire for breast reconstruction, which has known benefits psychosocially.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".