Previous diabetic ketoacidosis as a risk factor for recurrence in a large prospective contemporary pediatric cohort: Results from the <scp>DPV</scp> initiative
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Bibliographic record
Abstract
OBJECTIVE: To assess the role of previous episodes of diabetic ketoacidosis (DKA) and their time-lag as risk factors for recurring DKA in youth with type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: In a population-based analysis, data from 29,325 children and adolescents with T1D and at least 5 years of continuous follow-up were retrieved from the "Diabetes Prospective Follow-up" (DPV) multi-center registry in March 2020. Statistical analyses included unadjusted comparisons, logistic and negative binomial regression models. RESULTS: Among 29,325 patients with T1D, 86.0% (n = 25,219) reported no DKA, 9.7% (n = 2,833) one, and 4.3% (n = 1,273) more than one episode, corresponding to a DKA rate of 4.4 [95% CI: 4.3-4.6] per 100 patient-years. Female sex, migratory background, higher HbA1c values, higher daily insulin doses, a lower glucose monitoring frequency, and less CGM usage were associated with DKA. In patients with a previous episode, the DKA rate in the most recent year was significantly higher than in patients with no DKA (17.6 [15.9-19.5] vs. 2.8 [2.7-3.1] per 100 patient-years; p < 0.001). Multiple DKAs further increased the recurrence rate. The risk for DKA in the most recent year was higher in patients with an episode in the preceding year than in patients with no previous DKA (OR: 10.0 [95% CI: 8.6-11.8]), and remained significantly elevated 4 years after an episode (OR: 2.3 [1.6-3.1]; p < 0.001). CONCLUSIONS: Each episode of DKA is an independent risk factor for recurrence, even 4 years after an event, underlining the importance of a close follow-up after each episode.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it