Comorbidities and Quality of Life among Breast Cancer Survivors: A Prospective Study
Bibliographic record
Abstract
Many breast cancer survivors have coexistent chronic diseases or comorbidities at the time of their cancer diagnosis. The purpose of the study was to evaluate the association of comorbidities on breast cancer survivors' quality of life. A prospective design was used to recruit 140 women before cancer surgery, 134 women completed the study. Comorbidities were assessed using self-report and verified by medical record review and the Charlson Comorbidity Index (CCI) before and 12-month after cancer surgery. Quality of life was evaluated using Short-Form Health Survey (SF-36 v2). Descriptive statistics, chi-square tests, t-tests, Fisher's exact test, and correlations were performed for data analysis. A total of 28 comorbidities were identified. Among the 134 patients, 73.8% had at least one of the comorbidities, 54.7% had 2-4, and only 7.4% had 5-8. Comorbidities did not change at 12 months after surgery. Numbers of comorbidities by patients' self-report and weighted categorization of comorbidities by CCI had a similar negative correlation with overall quality of life scores as well as domains of general health, physical functioning, bodily pain, and vitality. Comorbidities, specifically hypertension, arthritis, and diabetes, were associated with poorer quality of life in multiple domains among breast cancer survivors. Future research should consider the combined influence of comorbidity and cancer on patients' quality of life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".