The Effects of Age and Comorbidity on Treatment and Outcomes in Women With Endometrial Cancer
Bibliographic record
Abstract
BACKGROUND: Although the incidence of endometrial cancer increases with age, the effect of patient age on treatment selection and outcomes is unclear. In addition, although aging is associated with increased prevalence of comorbid conditions, the extent to which comorbidities influence endometrial cancer management is not well documented. METHODS: This population-based analysis evaluates the effect of age and comorbidity on endometrial cancer treatment and outcome in a cohort of 401 patients referred to the Vancouver Island Centre, British Columbia Cancer Agency from 1989 to 1996. Treatment and 5-year actuarial overall survival (OS) and disease-free survival (DFS) were compared by age at diagnosis (<65, 65-74, and > or =75 years) and comorbidity index (Charlson score 0-1 and > or =2). RESULTS: Median follow-up time was 7.8 years. In this cohort, 148 (37%), 152 (38%), and 101 (25%) were aged <65, 65-74, and > or =75 years, respectively. Charlson comorbidity scores > or =2 were found in 18% of patients. Distributions of disease stage, tumor characteristics, and surgical therapy were similar across age and comorbidity subgroups. Standard surgery in this cohort comprised hysterectomy without routine lymphadenectomy. In stage Ic disease, the use of postoperative RT declined with advanced age (96%, 97%, and 74% in patients aged <65, 65-74, and > or =75 years, respectively, P = 0.05) and with increased comorbidities (91% and 79% in patients with Charlson score 0-1 and > or =2, respectively, P = 0.07). Among stage Ic patients aged > or =75 years, pelvic/vaginal relapse occurred in 2 of 6 patients treated with hysterectomy alone compared with 0 of 20 patients treated with postoperative radiotherapy (P = 0.006). On multivariable Cox modeling, age at diagnosis, performance status, stage, grade, lymphovascular invasion, surgery, and radiotherapy use, but not Charlson comorbidity score, were significant predictors for overall survival. CONCLUSIONS: Although surgical therapy for endometrial cancer was not influenced by age or comorbidities, reduced use of postoperative radiotherapy in stage Ic disease was observed among women with advanced age and high comorbidity index. The associated pelvic/vaginal relapse rates were higher in elderly patients not treated with radiotherapy. Chronologic age alone should not preclude patients from consideration of optimal local therapy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".