Real-world crude incidence of hypoglycemia in adults with diabetes: Results of the InHypo-DM Study, Canada
Bibliographic record
Abstract
OBJECTIVE: Very few real-world studies have been conducted to assess the incidence of diabetes-related hypoglycemia. Moreover, there is a paucity of studies that have investigated hypoglycemia among people taking secretagogues as a monotherapy or in combination with insulin. Accordingly, our research team developed and validated the InHypo-DM Person with Diabetes Mellitus Questionnaire (InHypo-DMPQ) with the aim of capturing the real-world incidence of self-reported, symptomatic hypoglycemia. The questionnaire was administered online to a national sample of Canadians (≥18 years old) with type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) treated with insulin and/or insulin secretagogues. RESEARCH DESIGN AND METHODS: Self-report data obtained from the InHypo-DMPQ were descriptively analyzed to ascertain the crude incidence proportions and annualized incidence densities (rates) of 30-day retrospective non-severe and 1-year retrospective severe hypoglycemia, including daytime and nocturnal events. RESULTS: A total of 552 people (T2DM: 83%; T1DM: 17%) completed the questionnaire. Over half (65.2%) of the total respondents reported experiencing at least one event (non-severe or severe) at an annualized crude incidence density of 35.1 events per person-year. The incidence proportion and rate of non-severe events were higher among people with T1DM versus T2DM (77% and 55.7 events per person-year vs 54% and 28.0 events per person-year). Severe hypoglycemia was reported by 41.8% of all respondents, at an average rate of 2.5 events per person-year. CONCLUSIONS: The results of the InHypo-DMPQ, the largest real-world investigation of hypoglycemia epidemiology in Canada, suggest that the incidence of hypoglycemia among adults with diabetes taking insulin and/or insulin secretagogues is higher than previously thought.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".