Combination of obesity and co-morbidities leads to unfavorable outcomes in COVID-19 patients
Bibliographic record
Abstract
Obesity has been described as a significant independent risk factors of COVID-19. We aimed to study the association between obesity, co-morbidities and clinical outcomes of COVID-19. Clinical data from 417 patients were collected retrospectively from the Al Kuwait Hospital, Ministry of Health and Prevention (MOHAP), Dubai, United Arab Emirates, who were admitted between March and June 2020. Patients were divided according to their body mass index (BMI). Various clinical outcomes were examined: presenting symptoms, severity, major co-morbidities, ICU admission, death, ventilation, ARDS, septic shock and laboratory parameters. The average BMI was 29 ± 6.2 kg/m2. BMI alone was not associated with the outcomes examined. However, class II obese patients had more co-morbidities compared to other groups. Hypertension was the most significant co-morbidity associated with obesity. Patients with BMI above the average BMI (29 kg/m2) and presence of underlying co-morbidities showed significant increase in admission to ICU compared to patients below 29 kg/m2 and underlying co-morbidities (21.7% Vs. 9.2%), ARDS development (21.7% Vs. 10.53%), need for ventilation (8.3% Vs. 1.3%), and mortality (10% Vs. 1.3%). Our data suggests that presence of underlying co-morbidities and high BMI work synergistically to affect the clinical outcomes of COVID-19.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".