Identifying people with multiple sclerosis in the Canadian Primary Care Sentinel Surveillance Network
Bibliographic record
Abstract
BACKGROUND: We validated a case definition for multiple sclerosis using a clinical cohort linked with the Manitoba Primary Care Research Network of the Canadian Primary Care Sentinel Surveillance Network, and applied this definition to describe multiple sclerosis epidemiology using the Canadian Primary Care Sentinel Surveillance Network repository. METHODS: We developed candidate case definitions for multiple sclerosis in the Manitoba Primary Care Research Network using diagnoses and medications. We compared these case definitions to multiple sclerosis diagnoses identified by applying a validated definition to population-based administrative data (reference standard 1) and multiple sclerosis diagnoses recorded by the provincial Multiple Sclerosis Clinic (reference standard 2) using sensitivity, specificity, positive predictive value and negative predictive value. We applied the preferred case definition to the national Canadian Primary Care Sentinel Surveillance Network dataset. RESULTS: The Manitoba Primary Care Research Network included 160,904 patients. The preferred case definition required ≥2 billing records for multiple sclerosis within 2 years or multiple sclerosis listed as a health condition or ≥1 multiple sclerosis-specific prescription. This definition had a low sensitivity versus administrative (44.25%) and clinic datasets (53.41%) but high specificity versus administrative data (99.95%). Specificity was lower versus clinic data (71.43%), but the positive predictive value was high. CONCLUSION: We developed a case definition for multiple sclerosis that can be applied to the Canadian Primary Care Sentinel Surveillance Network dataset for studies examining primary care of persons with multiple sclerosis.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".