Diabetic ketoacidosis at type 1 diabetes diagnosis in children during the <scp>COVID</scp> ‐19 pandemic
Bibliographic record
Abstract
OBJECTIVE: The COVID-19 pandemic has led to significant public health measures that have resulted in decreased acute pediatric care utilization. We evaluated whether the rate of severe presentations of new onset type 1 diabetes (DM1), such as, diabetic ketoacidosis (DKA) has changed since the COVID-19 public health measures were enacted. RESEARCH DESIGN AND METHODS: A retrospective chart review of children less than 18 years of age presenting with new onset DM1 during the pandemic period of March 17, 2020 to August 31, 2020 was conducted at two tertiary care pediatric hospitals in Alberta, Canada. Rates of DKA and severe DKA were compared to the same time period in the year 2019 (pre-pandemic control). RESULTS: The number of children presenting with newly diagnosed DM1 was similar during the pandemic year of 2020 compared with 2019 (107 children in 2020 vs. 114 in 2019). The frequency of DKA at DM1 onset was significantly higher in the pandemic period (68.2% vs 45.6%; p < 0.001) and incidence of severe DKA was also higher (27.1% in 2020 vs 13.2% in 2019; p = 0.01). CONCLUSIONS: There was a significant increase in DKA and severe DKA in children presenting with new onset DM1 during the COVID-19 pandemic period. This emphasizes the need for educating health care professionals and families to be aware of the symptoms of hyperglycemia and the importance of early diagnosis and treatment even during public health measures for COVID-19.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".