Identifying individuals with multiple sclerosis in an electronic medical record
Bibliographic record
Abstract
BACKGROUND: The increasing use of electronic medical records (EMRs) presents an opportunity to efficiently evaluate and improve quality of care for individuals with MS. OBJECTIVES: We aimed to establish an algorithm to identify individuals with MS within EMRs. METHODS: We used a sample of 73,003 adult patients from 83 primary care physicians in Ontario using the Electronic Medical Record Administrative data Linked Database (EMRALD). A reference standard of 247 individuals with MS was identified through chart abstraction. The accuracy of identifying individuals with MS in an EMR was assessed using information in the cumulative patient profile (CPP), prescriptions and physician billing codes. RESULTS: An algorithm identifying MS in the CPP performed well with 91.5% sensitivity, 100% specificity, 98.7% PPV and 100% NPV. The addition of prescriptions for MS-specific medications and physician billing code 340 used four times within any 12-month timeframe slightly improved the sensitivity to 92.3% with a PPV of 97.9%. CONCLUSIONS: Data within an EMR can be used to accurately identify patients with MS. This study has positive implications for clinicians, researchers and policy makers as it provides the potential to identify cohorts of MS patients in the primary care setting to examine quality of care.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".