Is there an optimal comorbidity index for prostate cancer?
Bibliographic record
Abstract
BACKGROUND: Comorbidity is an important consideration in oncology practice, particularly among older patients. Although a variety of comorbidity indices have been employed in research studies, it is unclear whether any one index is preferred. METHODS: An age-stratified random sample of 345 men (mean age of 69 years) who were newly diagnosed with prostate cancer were identified from a cancer registry in Ontario, Canada. Comorbidity and treatment information were obtained from chart review. Four comorbidity indices were utilized: Charlson Index, Diagnosis Count, Index of Coexistent Disease (ICED), and number of medications. Logistic regression analysis was used to compare the performance of comorbidity measures with respect to predicting receipt of curative treatment (radical prostatectomy or radiotherapy) and overall 6-year survival. Multivariable model performance including each of the comorbidity measures was compared by calculating the area under the receiver operating characteristic curve (AUROC). RESULTS: Among men with localized disease (n = 231), in models adjusted for age, Gleason score, and prostate-specific antigen level, only the Charlson Index was found to be a statistically significant predictor of receipt of curative treatment (P < .05), although all comorbidity indices had similar AUROC in adjusted models. After a median follow-up of 6.5 years, 116 of 345 men (33.6%) had died. In adjusted models, all 4 comorbidity indices performed similarly in predicting overall survival. CONCLUSIONS: Although comorbidity is an important predictor of both curative treatment and overall survival in prostate cancer, the optimal comorbidity index for use in research remains unclear. Selecting the optimal comorbidity index may depend on both the specific patient population and the outcome being considered.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".