Comparing body mass index and obesity‐related comorbidities as predictors in hospitalized <scp>COVID</scp>‐19 patients
Bibliographic record
Abstract
Summary The association between body mass index (BMI) and poor COVID‐19 outcomes in patients has been demonstrated across numerous studies. However, obesity‐related comorbidities have also been shown to be associated with poor outcomes. The purpose of this study was to determine whether BMI or obesity‐associated comorbidities contribute to elevated COVID‐19 severity in non‐elderly, hospitalized patients with elevated BMI (≥25 kg/m2). This was a single‐center, retrospective cohort study of 526 hospitalized, non‐elderly adult (aged 18–64) COVID‐19 patients with BMI ≥25 kg/m2 in suburban New York from March 6 to May 11, 2020. The Edmonton Obesity Staging System (EOSS) was used to quantify the severity of obesity‐related comorbidities. EOSS was compared with BMI in multivariable regression analyses to predict COVID‐19 outcomes. We found that higher EOSS scores were associated with poor outcomes after demographic adjustment, unlike BMI. Specifically, patients with increased EOSS scores had increased odds of acute kidney injury (adjusted odds ratio [aOR] = 6.40; 95% CI 3.71–11.05), intensive care unit admission (aOR = 10.71; 95% CI 3.23–35.51), mechanical ventilation (aOR = 3.10; 95% CI 2.01–4.78) and mortality (aOR = 5.05; 95% CI 1.83–13.90). Obesity‐related comorbidity burden as determined by EOSS was a better predictor of poor COVID‐19 outcomes relative to BMI, suggesting that comorbidity burden may be driving risk in those hospitalized with elevated BMI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".