Obesity and Disease Severity Among Patients With COVID-19
Bibliographic record
Abstract
Background Obesity can be associated with one or more co-morbidities that worsen the effect of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Studies demonstrated that severe forms of coronavirus disease (COVID-19) have occurred in elderly patients and patients with co-morbidities such as diabetes, hypertension, and cardiovascular diseases. Objective This study investigated the impact of obesity on COVID-19 severity, irrespective of other individual factors. Methods This retrospective observational study included all adult patients with confirmed COVID-19 infection, who were admitted to Sheikh Khalifa Ibn Zaid International University Hospital between March 20 and May 10, 2020. First, we compared patients with and those without obesity in terms of demographic characteristics, co-morbidities, clinical symptoms, and outcomes. Further, using logistic regression models, we analyzed the association between obesity and intensive care unit (ICU) admission. Also, we examined whether the association between obesity and ICU admission was also consistent among overweight patients. Results The study population included 107 patients with confirmed COVID-19 infection. Obese patients have been admitted in ICU more than patients without obesity (P-value = 0.035). While adjusting for other risk factors for ICU admission, we found that obesity was an independent risk factor for ICU admission (OR = 5.04, 95% CI (1.14-22.37)). When we examined the association of both obesity and overweight with ICU admission, we found that only obesity was significantly associated with ICU admission (OR = 9.11, 95% CI (1.49-55.84)). Conclusion Our study found that obesity was strongly associated with severity of COVID-19. The risk of ICU admission is greater in the presence of obesity. Physicians should be awarded to the need of specific and early management of obese patients with COVID-19 disease.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".