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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".