Identifying Ontarians with Type 2 Diabetes Mellitus in Administrative Data: A Comparison of Two Case Definitions
Bibliographic record
Abstract
INTRODUCTION: This study compared two previously validated sensitive and specific diabetes case definitions to explore the impact of different classification methods in Ontario ICES administrative data. METHODS: This study included patients captured by the Ontario Diabetes Database with type 2 diabetes using either the sensitive cohort definition (≥ 2 physician visits for diabetes within 1 year or ≥ 1 drug claim for diabetes or ≥ 1 hospitalization with diabetes), or the specific cohort definition (≥ 3 physician visits for diabetes within 1 year), between October 1, 2013 to September 30, 2015. Each cohort's demographic and clinical features were described using descriptive analysis. RESULTS: Using sensitive and specific definitions, 1,093,812 and 783,228 patients with type 2 diabetes were identified, respectively. Overall, the demographic and clinical characteristics were similar between cohorts. Patients in the sensitive cohort had mean age of 64.1 years and were 52.4% male, compared to 64.8 years and 53.6% male in the specific cohort. In the sensitive and specific cohorts respectively, 64.4% and 55.7% of patients reported one-year mean HbA1c of < 7% (53 mmol/mol) and 25.3% and 31.5% reported levels between 7.0-8.5% (53-69 mmol/mol). CONCLUSIONS: Although sample sizes were different between sensitive and specific cohorts, demographic and clinical characteristics were similar.
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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 | 0.022 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".