A comparison of charlson and elixhauser comorbidity measures to predict colorectal cancer survival using administrative health data
Bibliographic record
Abstract
BACKGROUND: Cancer survival is related to features of the primary malignancy and concurrent presence of nonmalignant diseases (comorbidities), including weight-related conditions (obesity, weight loss). The Charlson and Elixhauser methods are 2 well-known methods that take comorbidities into account when explaining survival. They differ in both the number and categorization of comorbidities. METHODS: Cancer, comorbidity, and survival data were acquired from inpatient administrative hospital records in 574 colorectal cancer patients. Robust Poisson regression was used to analyze 2- and 3-year survival according to cancer features and comorbidities classified by the Charlson and Elixhauser methods. Data for weight-related conditions (body mass index, weight loss) and performance status were acquired upon a new patient visit to the regional cancer center. Discrimination was assessed with the concordance (c) statistic. RESULTS: A base model (age, sex, stage) had excellent discrimination (c-statistic, 0.824 [2-year survival] and 0.827 [3-year survival]). The addition of Charlson comorbidities did not outperform the base model (c-statistic, 0.831 [2-year survival] and 0.833 [3-year survival]). Elixhauser comorbidities added higher discrimination compared with the base model, both in stage and overall (c-statistic, 0.852 [2-year survival] and 0.854 [3-year survival]; P < .01). The greatest increase in the c-statistic contributed by the addition of the Elixhauser comorbidities occurred in stage II patients (increased from 0.683 to 0.838). Overall, the Elixhauser comorbidities outperformed the Charlson comorbidities (P < .05). The use of self-reported weight and performance status data significantly increased discrimination by the Elixhauser method in 2-year but not 3-year survival. CONCLUSIONS: The Elixhauser method is a superior comorbidity risk-adjustment model for colorectal cancer survival prediction.
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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 | 0.054 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".