Measuring Comorbidity in Patients With Head and Neck Cancer
Bibliographic record
Abstract
BACKGROUND: Comorbidities are diseases or conditions that coexist with a disease of interest. The importance of comorbidities is that they can alter treatment decisions, change resource utilization, and confound the results of survival analysis. OBJECTIVE: The objective of this study was to determine the best comorbidity index to use in survival analysis of patients with squamous cell carcinoma of the head and neck. METHOD: Four validated indexes, with very different methodologies (i.e., the Charlson Index, the Cumulative Illness Rating Scale, the Kaplan-Feinstein Classification, the Index of Co-existent Disease), were tested using data from 379 unselected consecutive patients with complete 3-year follow-up from the Kingston Regional Cancer Center. Kaplan-Meier analysis and Cox Proportional Hazards Regression were used to stratify patients into three levels of increasing severity of comorbidity for each index. The Proportion of Variance Explained and Receiver Operating Characteristics curves were used to compare the performance of the indexes. CONCLUSION: The Kaplan-Feinstein Classification was the most successful in stratifying patients in this population.
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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 | 0.007 |
| 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 | 0.000 | 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".