Association of diabetes and severe covid-19 outcomes: A rapid review and meta-analysis
Bibliographic record
Abstract
Background: Addressing the urgent need for evidence on diabetes as a serious comorbidity for severe illness and death from coronavirus disease 2019 (COVID-19), we investigated the association between diabetes and COVID-19 disease severity in patients hospitalized due to COVID-19. Methods: This rapid review and meta-analysis was undertaken in adherence with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. MEDLINE and EMBASE were searched for studies published between January 1 and May 20, 2020. Studies included were English language, peer-reviewed, observational studies of adults hospitalized for COVID-19 with reported clinical course and living with or without diabetes. The severity of clinical course was assessed using a composite outcome (mortality, admittance to intensive care unit (ICU), requirement for invasive mechanical ventilation (IMV), clinically defined severe or critical disease). Data and adjusted measures of association were extracted from published reports, and meta-analysis was performed using a random effects model. The protocol was registered with OSF (). Results: A literature search yielded 431 articles, of which 45 studies (22,091 patients) met the inclusion criteria and 14 studies (12,383 patients) reported an adjusted measure of association for diabetes with the composite outcome with pooled hazard ratio 1.59 (95% confidence interval 1.3 - 1.93; I 2 = 0%, P = 0.820) and pooled odds ratio of 2.15 (95% confidence interval 1.63 - 2.83; I 2 = 0%, P = 0.892); evidence by GRADE was moderate. Conclusions: People living with diabetes are more likely to develop severe COVID-19 clinical course if hospitalized for COVID-19 than people not living with diabetes. To inform clinical decision-making during the pandemic, our findings support that people living with diabetes who are hospitalized for COVID-19 should be prioritized when triaged as at increased risk for the development of severe clinical course. J Endocrinol Metab. 2020;10(5):118-130 doi: https://doi.org/10.14740/jem698
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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 | 0.024 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.044 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".