Previous diabetic ketoacidosis as a risk factor for recurrence in a large prospective contemporary pediatric cohort: Results from the <scp>DPV</scp> initiative
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 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".