Variation between countries in the frequency of diabetic ketoacidosis at first presentation of type 1 diabetes in children: a systematic review
Bibliographic record
Abstract
AIMS/HYPOTHESIS: Type 1 diabetes is the most frequent endocrine disease in children, with 65,000 children diagnosed worldwide every year. Up to 80% of these children present with diabetic ketoacidosis (DKA), which is associated with both short-term risks and long-term consequences. This study aimed to characterise the worldwide variation in presentation of type 1 diabetes to inform future interventions to reduce this excess morbidity and mortality. METHODS: This was a systematic review of studies indexed on PubMed, EMBASE, Web of Science, Scopus or CINAHL before March 2011 that included unselected groups of children presenting with new-onset type 1 diabetes, reported the proportion presenting with DKA and used a definition of DKA based on measurement of pH or bicarbonate. RESULTS: Sixty-five studies of cohorts comprising over 29,000 children in 31 countries were included. The frequency of DKA at diagnosis ranged from 12.8% to 80%, with highest frequencies in the United Arab Emirates, Saudi Arabia and Romania, and the lowest in Sweden, the Slovak Republic and Canada. Multivariable modelling showed the frequency of DKA was inversely associated with gross domestic product, latitude and background incidence of type 1 diabetes. CONCLUSIONS/INTERPRETATION: This is the first description of the variation in frequency of DKA at presentation of type 1 diabetes in children across countries. It demonstrates large variations that may, at least in part, be explained by different levels of disease awareness and healthcare provision and suggests ways to decrease the excess morbidity and mortality associated with DKA at diagnosis.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".