A comparison of comorbidity measures for predicting one-year mortality in cancer patients.
Bibliographic record
Abstract
e17679 Background: Comorbidity is an independent predictor of cancer patient outcome. The Charlson and Elixhauser indices are the most common measures of comorbidity among cancer patients, but their relative predictive performance is unclear. We tested the association between these indices of comorbidity and survival among patients with breast, colorectal cancer, and lung cancer. Methods: Population-based administrative data from Manitoba Health, Health Living and Seniors (hospital discharge abstracts, physician billing claims, and prescription drug databases) housed at the Manitoba Centre for Health Policy were linked to the Manitoba Cancer. The study cohort included adults with breast, colorectal, or lung cancer made between 2004 and 2011. Comorbidity was measured by: Charlson index, Elixhauser index, Chronic Disease Score, number of different diagnoses, number of different prescription drugs, and Johns-Hopkins Aggregated Diagnostic Groups (ADGs). Logistic regression models with and without comorbidity measures were used to assess comorbidity measure discrimination (c-statistic), prediction error, and reclassification performance for one-year mortality. All models were adjusted for age, sex, region of residence, income quintiles, treatment, and cancer stage. Results: A total of 4984 breast, 4597 colorectal, and 4870 lung cancer patients were included. The base model without comorbidity covariates had excellent discrimination (c–statistics: 0.940 [breast], 0.890 [colorectal], and 0.868 [lung]). The addition of the Elixhauser index to the base model improved discrimination for breast (c-statistic = 0.947, Δc: +0.01%), and colorectal (c-statistic = 0.897, Δc: +0.01%) in one-year mortality, but all other measures of comorbidities did not add further discrimination. Elixhauser performed better than the other measures on all model statistics. Conclusions: Comorbidity is an important predictor of overall mortality but the incremental effect is small after accounting for patient and disease covariates. Only the Elixhauser index increased the discriminant performance and the effect was small.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".