Glycemic control in Children with Type 1 Diabetes During the COVID-19 Pandemic
Bibliographic record
Abstract
Background: Since March 2020, health systems around the world shifted to virtual care approaches as social distancing measures were recommended to stem the spread of SARS-COV-2, the virus responsible for the COVID-19 pandemic. For children and families living with type 1 diabetes, virtual consultations in pediatric diabetes care were rare prior to the pandemic but became the norm since the start of the pandemic. Data regarding glycemic outcomes and comorbidities in children living with type 1 diabetes mellitus (T1DM) during the pandemic are limited, and there is a need for these data to drive future care models design and delivery. Aim & Methods: The aim of this project was to assess the association of the COVID-19 pandemic with measures of glycemic control (HbA1c), hyperglycemia, hypoglycemia, diabetic ketoacidosis (DKA) and hospitalization for the period spanning March 2020-2021 at McMaster Children’s Hospital, a tertiary pediatric academic center in Hamilton, Ontario, Canada. Data from the onset of virtual care were compared with data from two years pre-pandemic. Results: The COVID-19 pandemic was not associated with changes in HbA1c (MD -0.14, p=0.058), hospitalization (OR 0.57, p=0.068), or hypoglycemia (OR 1.11, p=0.484), but was significantly associated with the increase in reported hyperglycemia (OR 1.38, p=0.003) and reduction in DKA presentation (OR 0.30, p=0.009). Conclusions: Glycemic control was stable during the early stages of the COVID-19 pandemic, when virtual and hybrid care models prevailed in diabetes care. These results suggest that patients and their families were able to adapt to the uncertain circumstances of the pandemic. Virtual consultations for pediatric diabetes did not hinder glycemic control, and likely aided in the maintenance of diabetes management. Longitudinal studies are necessary before virtual consultations should be recommended to replace in-person clinic visits, but the initial data seem encouraging.
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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 | 0.003 |
| 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 | 0.001 |
| 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".