Assessing the impact of comorbid illnesses on death within 10 years in prostate cancer treatment candidates
Bibliographic record
Abstract
BACKGROUND: Treatment choice in prostate cancer is influenced by pre-existing comorbid illnesses, but information about their individual prognostic impact is sparse, and only 1 comorbidity index has been developed for this setting. The authors assessed the impact of individual comorbid illnesses on the risk of early, other-cause death in prostate cancer treatment candidates and propose a modification of an existing comorbidity scale. METHODS: A population-based case-cohort study included patients diagnosed from 1990 through 1998 in Ontario, Canada who had planned curative radiotherapy or prostatectomy. The subcohort numbered 1643, and the case sample (those dying of other causes within 10 years) numbered 630. Ontario Cancer Registry data were linked to data from medical charts, including: age, comorbidity using the Cumulative Illness Rating Scale for Geriatrics (CIRS-G), stage, prostate-specific antigen, Gleason score, and treatment. Cox proportional hazards regression assessed the age-adjusted association between CIRS-G and other-cause death. RESULTS: Respiratory and cardiac diseases were the most common comorbidities and most strongly associated with an increased risk of death. Other important comorbidities included vascular disease, renal disease, and diabetes. The modified CIRS-G(pros) score yielded a relative risk (RR) of 1.64 (95% confidence interval [CI], 1.52-1.76) for those scoring 1 compared with 0 and RR 1.18 (95% CI, 1.15-1.21) for each increment above 1. Except for those aged >80 years, results were consistent across treatment type and age group. CONCLUSIONS: This study provides estimates of the role of individual comorbid illnesses in prostate cancer. The modified CIRS-G(pros) could be useful in the clinic and in future research on this patient population.
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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 | 0.000 | 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) | 0.001 | 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 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".