The COVID-19 and Dysglycemia Connection: Unveiling the Truth
Bibliographic record
Abstract
Abstract Background: It is well-established that COVID-19 is more prevalent among individuals with preexisting diabetes and can lead to worse outcomes. However, the question of whether COVID-19 contributes to the development of newly detected dysglycemia, including prediabetes and diabetes, remains uncertain. Objectives: To see the frequency and association of newly detected dysglycemia in COVID-19 infection.Materials and methods: This cross-sectional study was conducted in the Department of Endocrinology at BSMMU, spanned from March 2021 to September 2023. The research enrolled 177 participants, including 88 confirmed post COVID-19 patients and 89 individuals from a non-COVID-19 control group. Comprehensive sociodemographic, clinical, and laboratory data were collected, with a particular focus on oral glucose tolerance tests (OGTT) and HbA1c measurements. Results: The analysis revealed that there was no significant difference in the prevalence of newly detected dysglycemia between the two groups (COVID-19 vs Control: 35.2% vs 31.5%, P= 0.353).A statistically significant association was observed between the severity of COVID-19 and the development of newly detected dysglycemia (OR 3.68, 95% CI 1.03-14.46, P= .04). Age (38.25 ± 9.38 vs 32.5 ± 8.77, P=0.005), oxygen therapy (16.1% vs 1.8%, P=0.019), and steroid therapy (16.1% vs 3.5%, P= 0.037) also showed significant associations with newly detected dysglycemia. However, they didn't show any significance after adjusting withlogistic regression. Only age remained an independent predictor of newly detected dysglycemia (OR 1.074, 95% CI 1.008-1.145, P=0.027). Conclusions: This study observed there was no significant association of developing new dysglycemia in COVID-19 infected patient. Older age is more vulnerable to develop new dysglycemia in COVID-19 infected patient. Keywords: COVID-19, Dysglycemia, HbA1c. Impact of Employee Compensation and Benefits on Operating Performance This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Copyright © Author(s) retain the copyright of this article.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".