Health resource use in epilepsy: Significant disparities by age, gender, and aboriginal status
Bibliographic record
Abstract
PURPOSE: Epilepsy imposes a significant burden on society. The objective of this study was to estimate health resource utilization (HRU) over a 1-year period in epilepsy patients, using administrative databases. METHODS: Three administrative databases (inpatient, emergency, and physician claims) were used to identify epilepsy cases. HRU variables included general physician (GP) and emergency (ER) visits, physician billings, hospitalizations, and length of stay (LOS). Logistic regression was used to determine the association between demographic variables and HRU variations. RESULTS: Among the 1,431 patients with a mean age of 37.5 +/- 17.3 years, 56 (4%) were aboriginal. Ninety-six percent of patients saw a GP or a specialist (outpatient visit), 12% were hospitalized, and 8% visited the ER. Younger patients were more likely to see a neurologist (OR = 1.7, 95% CI 1.3-2.3), visit the ER (OR = 4.9, 95% CI 3.2-7.4), or be hospitalized (OR = 2.9, 95% CI 2.0-4.3). Females were less likely to see a GP but more likely to see a neurologist. Aboriginals were more likely than nonaboriginals to visit the ER (OR = 2.3, 95% CI 1.1-5.0) or be hospitalized (OR = 2.8, 95% CI 1.5-5.1) but less likely to see a neurologist (OR = 0.3, 95% CI 0.2-0.6). Welfare status and residence location (urban vs. rural) were not associated with HRU level. DISCUSSION: We demonstrated the feasibility of using administrative databases to assess HRU in epilepsy. We also uncovered disparities in HRU by age, gender, and by aboriginal status, suggesting possible internal or external barriers to specialized care in some groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".