PRESCRIBING PRACTICES IN CHILDHOOD EPILEPSY: USE OF A POPULATION HEALTH CARE DRUG DATABASE
Bibliographic record
Abstract
Objectives: Population based drug databases provide a cost effective means to study current prescribing patterns and changing trends in antiepileptic drug (AED) utilization. We describe the prescribing practices of physicians in Manitoba treating children with epilepsy in the first 6 years of life using a population drug database. Methods: A 1995 birth cohort was assembled for the province of Manitoba, Canada, from health care database records for a complete population in the setting of universal health care insurance, and linked with the prescription drug database. Children with actively treated epilepsy, as defined by the presence of an ICD-9 diagnosis code (345, 780,779) from a hospitalization, physician visit, with a concurrent prescription of an AED followed by subsequent usage of AED refills within twelve months, were identified over a 7 year period (1995–2001). Results: The prevalence of actively treated epilepsy in Manitoba children was 6.25/1000 with a mean age of diagnosis of 2.75 years (SD 1.89). The most commonly prescribed AEDs included: Phenobarbital (35.3%), Carbamazepine (25.6%) and Valproic Acid (18%). Newer anticonvulsants were prescribed less frequently. Only 22% of all prescriptions for AEDs were prescribed by a neurologist. A total of 6.1 AED prescriptions/epilepsy person years were prescribed. Continuous AED use was seen in 37% of all children. The total cost of AED drugs over the study period was $152/person/epilepsy person year. Conclusion: This is the first study to use a population-based approach to describe AED prescribing practices in Canadian children. The longitudinal pattern of AED selection and changes made during course of treatment were explored. Access to AEDs is influenced by location and regional disparities in level of health care expertise available.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".