The utility of the Edmonton Obesity Staging System for the prediction of COVID-19 outcomes: a multi-centre study
Bibliographic record
Abstract
Abstract Background Patients with obesity have an increased risk for adverse COVID-19 outcomes. Body mass index (BMI) does not acknowledge the health burden associated this disease. The performance of the Edmonton Obesity Staging System (EOSS), a clinical classification tool that assesses obesity-related comorbidity, is compared with BMI, with respect to adverse COVID-19 outcomes. Methods 1071 patients were evaluated in 11 COVID-19 hospitals in Mexico. Patients were classified into EOSS stages. Adjusted risk factors for COVID-19 outcomes were calculated and survival analysis for mechanical ventilation and death was carried out according to EOSS stage and BMI category. Results The risk for intubation was higher in patients with EOSS stages 2 and 4 (HR 1.42, 95% CI 1.02–1.97 and 2.78, 95% CI 1.83–4.24), and in patients with BMI classes II and III (HR 1.71, 95% CI 1.06–2.74, and 2.62, 95% CI 1.65–4.17). Mortality rates were significantly lower in patients with EOSS stages 0 and 1 (HR 0.62, 95% CI 0.42–0.92) and higher in patients with BMI class III (HR 1.58, 95% CI 1.03–2.42). In patients with a BMI ≥ 25 kg/m 2 , the risk for intubation increased with progressive EOSS stages. Only individuals in BMI class III showed an increased risk for intubation (HR 2.24, 95% CI 1.50–3.34). Mortality risk was increased in EOSS stages 2 and 4 compared to EOSS 0 and 1, and in patients with BMI class II and III, compared to patients with overweight. Conclusions EOSS was associated with adverse COVID-19 outcomes, and it distinguished risks beyond BMI. Patients with overweight and obesity in EOSS stages 0 and 1 had a lower risk than patients with normal weight. BMI does not adequately reflect adipose tissue-associated disease, it is not ideal for guiding chronic-disease management.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".