PROTOCOL: New‐Onset Diabetes Mellitus Post COVID‐19 Infection: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Background: Emerging evidence suggests that coronavirus disease 2019 (COVID-19) infection may cause diabetes mellitus in patients without a prior history of the condition. Objective: This review aims to determine the incidence of new-onset diabetes mellitus in COVID-19 patients compared to individuals without COVID-19, including rates of diabetic ketoacidosis, hyperglycaemia, mortality, and intensive care unit admission. Subgroup analyses will investigate patients receiving corticosteroid therapy for COVID-19, patients admitted to hospital due to COVID-19, and the incidence of new-onset diabetes mellitus in relation to diabetes mellitus worldwide. The incidence of new-onset diabetes mellitus post-infection after a 6-12-month follow-up will also be reported. Methods: This protocol follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines and is registered in PROSPERO (CRD42023457569). Eligible studies will include published and peer-reviewed cohort studies in English, after 1 December 2019. PubMed, Medline, Embase, and Scopus will be systematically searched. Included studies should compare new-onset diabetes mellitus incidence in COVID-19 patients against a control group without COVID-19. Two independent reviewers will extract data from included studies and assess risk of bias using the Newcastle-Ottawa quality assessment scale. A random-effects meta-analysis will compare the relative risk of new-onset diabetes mellitus post COVID-19 infection compared to non-infected individuals. Conclusion: The findings of this review will contribute to understanding the bidirectional relationship between COVID-19 and diabetes mellitus and inform clinical management strategies for patients at risk. Systematic Review Registration: PROSPERO CRD42023457569.
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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 | 0.051 | 0.096 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.020 | 0.019 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.093 | 0.008 |
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".