Increasing prevalence of diabetic ketoacidosis at diabetes diagnosis among children in Quebec: a population-based retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Diabetic ketoacidosis at type 1 diabetes diagnosis is a preventable life-threatening complication. Canadian data on the temporal trends of the prevalence of diabetic ketoacidosis at the onset of type 1 diabetes in children are unknown. We aimed to determine the temporal changes in diabetic ketoacidosis prevalence at diabetes diagnosis in Quebec. METHODS: We conducted a population-based cohort study of children (aged 1-17 yr) living in Quebec who were diagnosed with diabetes between 2001 and 2014, using multiple health administrative linked databases available at the Institut national de santé publique du Québec through the Quebec Integrated Chronic Surveillance System. We used multivariate Poisson regression analysis with robust error variance to determine trends in the prevalence of diabetic ketoacidosis. RESULTS: We found that 25.6% (1471/5741) of children presented with diabetic ketoacidosis at diabetes diagnosis. The incidence of diabetes was stable at 30 cases per 100 000 children per year during the study period. The age- and sex-standardized rates of diabetic ketoacidosis increased from 22% (95% confidence interval [CI] 17%-26%) in 2001 to 30% (95% CI 24%-36%) in 2014. The relative increase of diabetic ketoacidosis prevalence at diabetes diagnosis over the study period was 2.0% per year (rate ratio 1.02; 95% CI 1.01-1.03). INTERPRETATION: Despite a stable incidence of type 1 diabetes, we found that the prevalence of diabetic ketoacidosis at diabetes onset increased between 2001 and 2014. Our findings are concerning and demonstrate a need to continue to campaign to recognize type 1 diabetes before diabetic ketoacidosis supervenes.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".