Temporal variation of diabetic ketoacidosis and hypoglycemia in adults with type 1 diabetes: A nationwide cohort study
Bibliographic record
Abstract
BACKGROUND: Seasonality in health outcomes has long been recognized for conditions such as colds and flus. The aim of the present study was to determine whether hospitalizations for acute complications of type 1 diabetes (T1D) vary by month and season. METHODS: An observational study was performed of national administrative health data. Hospitalizations for acute complications in adults (aged ≥18 years) with T1D were identified using ICD-10 (Canadian revision) codes between 2004 and 2010. Monthly and seasonal counts per year were determined for the study period. For each acute complication, the ratio of the number of observed hospitalizations/expected number of hospitalizations was calculated for each month and season per year, adjusting for varied lengths of month, season, and year. RESULTS: In all, there were 21 568 hospitalizations for diabetic ketoacidosis (DKA) and 5349 hospitalizations for hypoglycemia during the study period. December had higher than expected hospitalizations for DKA and March had higher than expected hospitalizations for hypoglycemia. There did not appear to be variation for either DKA or hypoglycemia hospitalizations by season. CONCLUSIONS: The results of the present study suggest temporal variation in hospitalizations for DKA and hypoglycemia, and therefore signal important times of patient vulnerability. Potential mechanisms underlying this pattern warrant further examination. Prevention strategies and resources for patients with T1D may need to be increased at specific times during the year.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".