Predictive validity of five comorbidity indices in prostate carcinoma patients treated with curative intent
Bibliographic record
Abstract
BACKGROUND: Comorbidity is important to consider in clinical research on curative prostate carcinoma because of the role of competing risks. Five chart-based comorbidity indices were assessed for their ability to predict survival. METHODS: This was a case-cohort study of prostate carcinoma patient cohort treated with curative intent in Toronto and Southeast Cancer Care Ontario regions between 1990 and 1996; the subcohort was drawn from these men, whereas cases were cohort members who died from causes other than prostate carcinoma. Comorbidity data were obtained from medical charts (269 subjects). Vital status, age, area of residence, and socioeconomic status information were available. Predictive validity was quantified by the percent variance explained (PVE) over and above age using proportional hazards modeling. RESULTS: The Chronic Disease Score (CDS) (PVE = 11.3%; 95% confidence interval [95% CI], 3.5-22.8%), Index of Coexistent Disease (ICED) (PVE = 9.0%; 95% CI, 2.9-17.9%), Cumulative Illness Rating Scale (CIRS) (PVE = 7.2%; 95% CI, 1.4-17.1%), Kaplan-Feinstein Index (PVE = 4.9%; 95% CI, 0.6-12.8%), and Charlson Index (PVE = 3.8%; 95% CI, 0.3-10.9%) each explained some outcome variability beyond age. PVE differences among indices were not statistically significant. A comorbidity identified at the time of cancer diagnosis was the cause of death in 59.2% of cases (75% for cardiac or vascular causes). CONCLUSIONS: The better-performing, more comprehensive indices (CDS, ICED, and CIRS) would be useful in measuring and controlling for comorbidity in this setting. The CDS was easiest to apply and explained the most outcome variability.
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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 | 0.002 | 0.011 |
| 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) | 0.001 | 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".