1240-P: Real-World Demographics and Clinical Characteristics of Type 2 Diabetes Mellitus Patients in Ontario, Canada
Bibliographic record
Abstract
Population-based studies of diabetes may be limited by sampling and capture. Our study utilizes the Ontario Diabetes Database, which captures all persons in Ontario, Canada with non-gestational diabetes. Based on sensitive and specific cohort definitions adults with T2DM were identified. Study objectives are to investigate: the demographic and the clinical characteristics of type 2 diabetes patients in Ontario, Canada over a time frame from Apr 1, 2002 to Sept 31, 2017. We also compare the demographics and clinical characteristics of T2DM patient populations from the cohorts to understand the impact T2DM definitions have on characterizing the disease. We identify 1,093,812 and 783,228 in the cohorts (Table 1) . In the sensitive and specific cohorts, the mean age of a patient with T2D is 64 to 65 years old and 52 to 54% are male, respectively. Roughly 56 to 64% of patients have a 1-year mean HbA1c of <7%, with ∼ 25 to 32% at 7.0%-8.5%, respectively. Overall, covariates of interest in our demographics and clinical characteristics analysis are similar for both cohorts. Our results show multiple comorbidities in this population; hypertension (∼ 77-79%) , dyslipidemia (∼55%) , chronic ischemic heart disease (∼28%) and angina (∼21%) . The comorbidities further illustrate a substantial proportion of T2DM patients suffer comorbid heart diseases. Disclosure R.L. Houlden: Advisory Panel; Novo Nordisk Canada Inc. Research Support; AstraZeneca. Speaker's Bureau; Abbott Diagnostics, Boehringer Ingelheim International GmbH, Dexcom, Inc., Eli Lilly and Company, Medtronic, Novo Nordisk Canada Inc., Sanofi. S. Golden: Other Relationship; Novo Nordisk Canada Inc. W. Shi: Other Relationship; Novo Nordisk Canada Inc. A.A. Kukaswadia: Other Relationship; Novo Nordisk. A. Sharma: Other Relationship; Novo Nordisk Canada Inc. K. Quansah: Employee; Boehringer Ingelheim (Canada) Ltd., Novo Nordisk Canada Inc. A.R. Liu: Employee; Novo Nordisk A/S, Novo Nordisk Canada Inc.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".