The Canadian Hypoglycemia Assessment Tool Program: Insights Into Rates and Implications of Hypoglycemia From an Observational Study
Bibliographic record
Abstract
OBJECTIVE: The true prevalence of hypoglycemia in insulin-treated patients with diabetes and its impact on patients, employers and healthcare providers is poorly appreciated owing to a paucity of real-world data. The global Hypoglycemia Assessment Tool (HAT) study addressed this issue, and here we report data from the Canadian cohort of patients. METHODS: This noninterventional, 6-month retrospective and 4-week prospective study enrolled patients aged ≥18 years receiving insulin treatment for >12 months from community endocrinology practices. Data were collected using self-assessment questionnaires and patient diaries. The primary endpoint was the proportion of patients experiencing ≥1 hypoglycemic event during the 4-week prospective observational period. RESULTS: Four hundred ninety-eight patients with type 1 diabetes (n=183) and type 2 diabetes (n=315) were enrolled. The prevalence of hypoglycemia was similar in the retrospective (type 1 diabetes, 92.3%; type 2 diabetes, 63.5%) and prospective (type 1 diabetes, 95.2%; type 2 diabetes, 64.2%) periods. Prospective rates of any, nocturnal and severe hypoglycemia per patient-year (95% confidence interval) were 69.3 (66.4; 72.2), 14.2 (12.9; 15.6) and 1.8 [1.4; 2.4]. Higher rates were reported retrospectively, reaching significance for nocturnal hypoglycemia per patient-year (30.0 [28.1; 32.0] vs. 14.2 [12.9; 15.6]; p<0.001). Hypoglycemia led to increased healthcare utilization and absenteeism and was associated with potentially harmful self-care behaviours (e.g., reduced or skipped insulin doses) and increased blood glucose self-monitoring. CONCLUSIONS: Prevalence and incidence of hypoglycemia were high among insulin-treated patients with diabetes in Canada, and some patients took harmful or costly actions when they experienced hypoglycemia. Identifying the insulin-treated patients who are at greatest risk may help to reduce the incidence of hypoglycemia.
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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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".