Comparison of the HAT study, the largest global hypoglycaemia study to date, with similar large real‐world studies
Bibliographic record
Abstract
AIMS: Optimal diabetes care requires clear understanding of the incidence of hypoglycaemia in real-world clinical practice. Current data on hypoglycaemia are generally limited to those reported from randomised controlled clinical trials. The Hypoglycaemia Assessment Tool (HAT) study, a non-interventional real-world study of hypoglycaemia, assessed hypoglycaemia in 27 585 individuals across 24 countries. The present study compared the incidence of hypoglycaemia from the HAT study with other similarly designed, large, real-world studies. MATERIALS AND METHODS: A literature search of PubMed (1995-2017) for population-based studies of insulin-treated patients with type 1 or type 2 diabetes (T1D, T2D), excluding clinical trials and reviews, identified comparable population-based studies reporting the incidence of hypoglycaemia. RESULTS: The 24 comparative studies, including more than 24 000 participants with T1D and more than 160 000 participants with T2D, varied in design, size, inclusion criteria, definitions of hypoglycaemia and method of recording hypoglycaemia. Reported rates (events per patient-year [PPY]) of hypoglycaemia were higher in patients with T1D than in those with T2D (overall T1D, 21.8-73.3 and T2D, 1.3-37.7; mild/non-severe T1D, 29.0-126.7 and T2D, 1.3-41.5; severe T1D, 0.7-5.8 and T2D, 0.0-2.5; nocturnal T1D, 2.6-11.3 and T2D, 0.38-9.7) and were similar to the ranges found in the HAT study. CONCLUSIONS: The HAT data on hypoglycaemia incidence were comparable with those from other real-world studies and indicate a high incidence of hypoglycaemia among insulin-treated patients. Differences in rates among studies are mostly explained by differences in patient populations and study methodology. The goal of reducing hypoglycaemia should be a target for continued educational and evidence-based pharmacological interventions.
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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 | 0.047 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".