Acute Effect of COVID-19 Vaccination on Glycemic Profile in Patients With Type 1 Diabetes Mellitus
Bibliographic record
Abstract
Background: Coronavirus disease 2019 (COVID-19) affected the whole world socially, economically, and medically. People with diabetes mellitus could have higher rates of morbidity and mortality if infected by the virus. New-onset diabetes and diabetic emergencies were, in some cases, first identified after the COVID-19 vaccine. In this study, we aimed to evaluate the acute effect of COVID-19 vaccination on the glycemic parameters of patients with type 1 diabetes mellitus (T1DM). Methods: This was a retrospective observational study that included patients with T1DM older than 14 years old using Freestyle libre sensors and vaccinated with the COVID-19 vaccine. Data were collected from patients' electronic medical records and glycemic profile parameters 1 to 2 weeks before and 1 to 2 weeks after the vaccine were extracted from the LibreView system. Results: Seventy-two vaccines were analyzed from 44 patients with T1DM. There was no acute change of interstitial glucose measures after COVID-19 vaccination; however, there was a significant reduction in time in range and an increase in time above range after vaccination in those who were aged above 28 and had longer duration and better diabetes mellitus (DM) control. Glycated hemoglobin (HbA1c) was the only independent factor associated with the change in the glycemic profile after vaccination in multivariate regression analysis. Conclusion: In our study population, there was no significant change in glycemic parameters after COVID-19 vaccination but those above 28 years old with longer duration of DM and lower HbA1c could have shifted upward their interstitial glucose levels. Precautions could be considered in those groups before receiving the vaccine. J Endocrinol Metab. 2023;13(4):144-152 doi: https://doi.org/10.14740/jem894
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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 | 0.001 |
| Bibliometrics | 0.000 | 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".