The performance of three mortality risk‐adjustment comorbidity indices in a community epilepsy cohort
Bibliographic record
Abstract
Mortality risk-adjustment comorbidity indices are an efficient means of controlling for the important confounding effect of somatic and psychiatric comorbidities in observational mortality studies. We carried out an external validation study and compared the performance of the Charlson, Elixhauser and Epilepsy-specific (ES) indices using the National General Practice Study of Epilepsy, a community-based prospective cohort of 558 people with incident epilepsy followed for 23.3 years (median). The minimum and maximum crude mortality rates were similar between the three indices, but mid-range Elixhauser scores predicted lower rates relative to the two other indices. Two of the stratified Charlson Kaplan-Meier survival probability curves crossed, and a low Elixhauser score was associated with a counterintuitive increase in mortality. Each comorbidity index was a significant predictor of mortality in the Cox proportional hazards models, although there was evidence that the unadjusted Charlson regression model violated the proportionality assumption. Harrell's c-statistics were >0.87 in all adjusted models. All three indices performed well, but there is evidence that the ES index may be more discriminating and have a better model fit than the Charlson or Elixhauser indices in a community-based clinical cohort of people with epilepsy.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".