Improving Epilepsy Care in Ontario, Canada: The Impact of a Provincial Strategy for Epilepsy Care
Bibliographic record
Abstract
OBJECTIVE: In 2016, the Ontario Ministry of Health and Long-Term Care implemented the Provincial Strategy for Epilepsy Care to increase epilepsy surgery use in Ontario, Canada. The objectives of this study were to assess whether the use of (1) epilepsy surgery, including (a) its receipt and (b) assessments for candidacy, and (2) other healthcare for epilepsy, including (a) neurological consultations, (b) emergency department (ED) visits and (c) hospital admissions, changed since its implementation. METHODS: of each year from 2007 to 2019, comprising patients with drug-resistant epilepsy eligible for publicly funded prescription drug coverage with no cancer history. We used segmented Poisson regression models to assess whether the annual rates of each outcome changed between the period before the Provincial Strategy was implemented (July 2007-June 2016) and the period after. RESULTS: There was a level increase in the rate of epilepsy surgery of 48% (95% CI: 0%, 118%) and slope decreases in the rates of neurological consultations, ED visits and hospital admissions for epilepsy of 10% (95% CI: -15%, -5%), 10% (95% CI: -20%, 1%) and 7% (95% CI: -12%, -1%) per year, respectively, associated with the Provincial Strategy. CONCLUSION: The Provincial Strategy may be associated with an increased rate of epilepsy surgery and reduced rates of other healthcare use for epilepsy. Other regions experiencing low epilepsy surgery rates may benefit from similar interventions.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".