Rates and predictors of hypoglycaemia in 27 585 people from 24 countries with insulin‐treated type 1 and type 2 diabetes: the global <scp>HAT</scp> study
Bibliographic record
Abstract
AIMS: To determine the global extent of hypoglycaemia experienced by patients with diabetes using insulin, as there is a lack of data on the prevalence of hypoglycaemia in developed and developing countries. METHODS: This non-interventional, multicentre, 6-month retrospective and 4-week prospective study using self-assessment questionnaire and patient diaries included 27 585 patients, aged ≥18 years, with type 1 diabetes (T1D; n = 8022) or type 2 diabetes (T2D; n = 19 563) treated with insulin for >12 months, at 2004 sites in 24 countries worldwide. The primary endpoint was the proportion of patients experiencing at least one hypoglycaemic event during the observational period. RESULTS: During the prospective period, 83.0% of patients with T1D and 46.5% of patients with T2D reported hypoglycaemia. Rates of any, nocturnal and severe hypoglycaemia were 73.3 [95% confidence interval (CI) 72.6-74.0], 11.3 (95% CI 11.0-11.6) and 4.9 (95% CI 4.7-5.1) events/patient-year for T1D and 19.3 (95% CI 19.1-19.6), 3.7 (95% CI 3.6-3.8) and 2.5 events/patient-year (95% CI 2.4-2.5) for T2D, respectively. The highest rates of any hypoglycaemia were observed in Latin America for T1D and Russia for T2D. Glycated haemoglobin level was not a significant predictor of hypoglycaemia. CONCLUSIONS: We report hypoglycaemia rates in a global population, including those in countries without previous data. Overall hypoglycaemia rates were high, with large variations between geographical regions. Further investigation into these differences may help to optimize therapy and reduce the risk of hypoglycaemia.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".