1003-P: Incidence of Severe Diabetic Ketoacidosis among Children with Type 1 Diabetes Mellitus Prior to and During COVID-Pandemic: A Meta-analysis
Bibliographic record
Abstract
Aim: The COVID-pandemic has affected access to healthcare services worldwide, including the pediatric population. This systematic review and meta-analysis aimed to estimate the risk of severe diabetic ketoacidosis (DKA) among children with type 1 diabetes (T1DM) during the COVID-pandemic compared pre-pandemic era. Methods: PubMed, EMBASE, and the Elsevier Coronavirus Research Repository Hub were searched for relevant observational studies. Studies published as an abstract or in non-English language were excluded. The primary outcome is the risk of severe DKA among children with T1DM during the COVID-pandemic compared to the prior-to-COVID-group. The second outcome is the risk of severe DKA among children with newly diagnosed T1DM. A random meta-analysis model was performed using R version 4.0.4 to estimate the relative risk of severe DKA. Results: A total of 18 studies were included in this metanalysis. Severe DKA risk was 76% (RR 1.76, 95%CI 1.33-2.33, I2=44%) higher during the COVID-pandemic than the pre-COVID-period. Among patients with newly diagnosed T1DM, the risk of severe DKA was 44% higher for the during-COVID-group (RR 1.44, 95%CI 1.26-1.65; I2=64%) . The bias assessment of the included studies using the Newcastle-Ottawa Scale (NOS) showed that all studies had quality indicators (>7 points) . In addition, the results of Eager’s test did not show potential for publication bias. Conclusions: This study showed that severe DKA risk had increased significantly during the COVID-pandemic compared to the pre-pandemic period. Disclosure O.Alfayez: None. J.Alfallaj: None. A.R.Almutairi: None.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.071 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".