Correlation of Diabetes Mellitus and COVID-19: A Review
Bibliographic record
Abstract
In the early pandemic, it was brought to attention that individuals with Diabetes Mellitus (DM) are prone to a more severe form of Coronavirus Disease 2019 (COVID-19). With this in mind, healthcare professionals need to be vigilant about their patients’ medical history in these challenging times as this could change the course of treatment and follow-ups for someone with COVID-19 and DM. Moreover, this is of utmost importance as the coronavirus is deemed to thrive in the elevated blood glucose environment. Though there is little known about the best medications for glycemic control in COVID-19, however, there are multiple other factors that can be beneficial in exploring for management in DM patients who are infected with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and some of these factors are as follow adequate glycemic control, medication dosages adjustment, diet, physical guidelines, thromboembolism prophylaxis, and empirical treatments for the possible co-infections. We conducted a literature review of publicly available information to summarize knowledge about the correlation between Diabetes Mellitus and the COVID-19 infections. The main objective of this manuscript is to provide a brief overview of the potential pathophysiologic correlation of DM and COVID-19, optimal management and prevention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".