Development of an epilepsy-specific risk adjustment comorbidity index
Bibliographic record
Abstract
PURPOSE: To develop an epilepsy-specific comorbidity risk adjustment index for mortality outcomes research. METHODS: Data were extracted from five linked administrative databases in Calgary, Canada from April 1, 1996 to March 31, 2004. Epilepsy patients were defined using a validated ICD-9-CM- and ICD-10-CA-based case definition. An epilepsy-specific comorbidity index was developed using comorbidities from the Charlson and Elixhauser indexes and other relevant epilepsy comorbidities. In the final model, 14 comorbidities significantly associated with mortality remained and each was assigned a value of 1-6 based on the hazard ratio from the survival analysis. Total prognostic scores were calculated and compared for all subjects using the epilepsy-specific index and the Charlson index. Crude mortality and survival curves of both indices were compared. KEY FINDINGS: We identified 7,253 subjects who met our case definition for epilepsy. The mean age of participants was 38 years (range 0.03-96), and 52% were male. The mortality rate was 7.9%. High rates of chronic pulmonary disease (20.3%), hypertension (19.6%), cerebrovascular disease (13.7%), fracture (12.1%), depression (28.2%), and alcohol abuse (10.1%) were noted. Patients with lower total prognostic scores were more likely to survive than patients with higher scores, using both indices. However, increasing prognostic scores were more strongly associated with reduced survival using the epilepsy-specific index compared to the Charlson index. SIGNIFICANCE: A new comorbidity index for epilepsy, designed to include clinically relevant conditions, provided better discrimination of crude mortality in a population-based group of epilepsy patients compared with the Charlson index.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".