Increase in new-onset type 1 diabetes diagnoses among Brazilian children and adolescents during the COVID-19 pandemic
Bibliographic record
Abstract
OBJECTIVE: This study aimed to determine if there was a rise in new T1DM cases in children during the pandemic in a large metropolitan area in Brazil. METHODS: The authors conducted a cross-sectional study at five public tertiary care centers that specialize in diabetes in children, comparing all new T1DM cases (ages 0.5-18y) diagnosed from March 2020 to December 2021 (pandemic period, PP) with those from March 2018 to December 2019 (historical period, HP). RESULTS: There were 167 new cases in the PP compared to 99 in the HP, reflecting a 68.7 % rise, with a notable peak observed in the third quarter of 2020 (p = 0.006). The average age of diagnosis was 8.4 ± 4.2 years in the PP and 7.5 ± 3.6 years in the HP, with no significant difference (p = 0.06). The gender distribution, BMI Z scores, and duration of diabetes symptoms before diagnosis were similar. The incidence of diabetic ketoacidosis (DKA) at onset was elevated but did not increase during the pandemic (62.6 % historical vs. 59.3 % pandemic period). During the PP, 24 % of patients reported symptoms of SARS-CoV-2 infection before the diagnosis of T1DM or at admission, and 13 % (7/53) of tested patients were positive for SARS-CoV-2. CONCLUSIONS: The present findings indicate a significant rise in new T1DM cases among children during the COVID-19 pandemic compared to prior years, without differences in DKA at onset.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".