Assessing the validity of using administrative data to identify patients with epilepsy
Bibliographic record
Abstract
OBJECTIVE: Previous validation studies assessing the use of administrative data to identify patients with epilepsy have used targeted sampling or have used a reference standard of patients in the neurologist, hospital, or emergency room setting. Therefore, the validity of using administrative data to identify patients with epilepsy in the general population has not been previously assessed. The purpose of this study was to determine the validity of using administrative data to identify patients with epilepsy in the general population. METHODS: A retrospective chart abstraction study was performed using primary care physician records from 83 physicians distributed throughout Ontario and contributing data to the Electronic Medical Record Administrative data Linked Database (EMRALD) A random sample of 7,500 adult patients, from a possible 73,014 eligible, was manually chart abstracted to identify patients who had ever had epilepsy. These patients were used as a reference standard to test a variety of administrative data algorithms. RESULTS: An algorithm of three physician billing codes (separated by at least 30 days) in 2 years or one hospitalization had a sensitivity of 73.7% (95% confidence interval [CI] 64.8-82.5%), specificity of 99.8% (95% CI 99.6-99.9%), positive predictive value (PPV) of 79.5% (95% CI 71.1-88.0%), and negative predictive value (NPV) of 99.7% (95% CI 99.5-99.8%) for identifying patients who had ever had epilepsy. SIGNIFICANCE: The results of our study showed that administrative data can reasonably accurately identify patients who have ever had epilepsy, allowing for a "lifetime" population prevalence determination of epilepsy in Ontario and the rest of Canada with similar administrative databases. This will facilitate future studies on population level patterns and outcomes of care for patients living with epilepsy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".