1672-P: Identifying Type 1 Diabetes in Electronic Medical Records Data in Ontario: A Validation Study
Bibliographic record
Abstract
Electronic Medical Record (EMR) data are an efficient method for constructing large type 1 diabetes (T1D) cohorts but are limited by availability of accurate diagnosis codes. We sought to derive an algorithm for identifying T1D using Electronic Medical Records - Primary Care (EMRPC) also known as EMRALD, a database including records from >300,000 primary care patients in Ontario, Canada. A 25% random sample of adults with diabetes and at least 1 year of records in EMRPC prior to September 30, 2015 formed the reference cohort. Charts of potential T1D cases (those on insulin±metformin) were abstracted; all others were classified as non-T1D. Algorithms were derived using variables from free-text searches (diagnosis terms in the Cumulative Patient Profile (CPP)) and standardized fields (medications, age, BMI) using 1)a priori combinations of variables; 2)classification and regression tree analysis (CART). Algorithms were evaluated for sensitivity, specificity, positive and negative predictive values (PPV and NPV) with adjustment for optimism for CART. Of 21,547 eligible patients with diabetes, the reference cohort was 5407: 4968 non-abstracted non-T1D, 240 abstracted non-T1D and 199 T1D. The prevalence of T1D was 3.7%. The optimal algorithm using a priori variable combinations was T1D diagnosis in the CPP + rapid-acting insulin (sensitivity 69.3% (95% CI 62.4-75.7%), specificity 99.8% (99.6-99.9), PPV 92.6% (87.2-96.3), NPV 98.8% (98.5-99.1)). CART partitioned on T1D diagnosis in the CPP, any insulin, and non-metformin oral hypoglycemic medications and improved performance with optimism-adjusted sensitivity of 77% (72.0-83.9), specificity 99.8% (99.8-100.0), PPV 96.8% (94.6-99.6), and NPV 99.1% (98.9-99.4). Simple algorithms using EMR variables yielded good diagnostic performance for identification of T1D and CART further improved performance. Pending additional validation these algorithms can be applied to study large T1D cohorts in EMR databases. Disclosure A. Weisman: None. J. Young: None. M. Kumar: None. P. Austin: None. K. Tu: None. L. Jaakkimainen: None. L. Lipscombe: None. G. Booth: None. Funding Diabetes Action Canada
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".