Canadian Administrative Health Data Can Identify Patients with Myasthenia Gravis
Bibliographic record
Abstract
INTRODUCTION: Incidence and prevalence estimates for myasthenia gravis (MG) have varied widely, and the ability of administrative health data (AHD) records to accurately identify cases of MG is yet to be ascertained. The goal of the current study was to validate an algorithm to identify patients with MG in Ontario, Canada using AHD - thereby enabling future disease surveillance. METHODS: A reference standard population was established using automated key word searching within EMRALD (Electronic Medical Record Administrative data Linked Database) and chart review of potential cases. AHD algorithms were generated and tested against the reference standard. The data was used to calculate MG prevalence rates. RESULTS: There were 123,997 eligible adult patients, and 49 patients had definite MG (forming the reference standard). An algorithm requiring: (1 hospital discharge abstract with MG listed as a reason for hospitalization or a comorbid condition), or (5 outpatient MG visits and 1 relevant diagnostic test, within 1 year), or (3 pyridostigmine prescriptions, within 1 year) identified MG with sensitivity = 81.6%, specificity = 100%, positive predictive value = 80.0% and negative predictive value = 100%. The population prevalence within our cohort was 0.04%. CONCLUSIONS: This novel validation method demonstrates the feasibility of using administrative health data to identify patients with myasthenia gravis among the Ontario population.
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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) | 0.001 | 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".