Identifying Individuals with Multiple Sclerosis in an Electronic Medical Record (P3.138)
Bibliographic record
Abstract
OBJECTIVE: To identify the optimal algorithm for accurately identifying individuals with multiple sclerosis (MS) in an electronic medical record (EMR). BACKGROUND: The increasing use of EMRs presents a unique opportunity to efficiently evaluate and improve quality of care for individuals with MS. EMRs provide more detailed clinical information than administrative databases. Additionally, primary care EMRs are less costly and provide a broader picture of MS patient management compared to information in subspecialty clinic registries. We set out to establish an algorithm to identify individuals with MS in an EMR to facilitate future studies. DESIGN/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 sample of 247 individuals with MS was identified through chart abstraction. The accuracy of identifying individuals with MS was assessed through testing algorithms that included various combinations of information in the cumulative patient profile (CPP), MS-specific prescriptions and physician billing codes for MS (code 340). RESULTS: The optimal algorithm was MS listed in the CPP, prescriptions for MS-specific medications or physician billing code 340 used at least four times. It performed with a sensitivity of 86.6% (95% confidence interval (CI) 81.8-90.6), specificity of 99.9% (95%CI 99.9-99.9), positive predictive value of 80.1% (95%CI 74.9-84.8), negative predictive value of 100% (95%CI 99.9-100) compared to the reference standard of MS status as recorded in the EMR. 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 study patterns of care including symptom onset to diagnosis and screening for and management of comorbid conditions in MS patients compared to control patients. Study supported by: A grant from the Canadian Institutes of Health Research (CIHR).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 0.001 |
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".