Predicting COVID-19 outcomes with the Edmonton Obesity Staging System
Bibliographic record
Abstract
BACKGROUND: Multiple studies have demonstrated a correlation between a high body mass index and discriminatory COVID-19 outcomes. Studies appear to indicate that there is a correlation between obesity-related comorbidities and less favorable outcomes. OBJECTIVES: The primary aim of the current investigation is to conduct a thorough assessment of the correlation between BMI and comorbidities associated with obesity, and their potential impact on the severity and consequences of COVID-19 infection among patients receiving care in a tertiary healthcare setting. DESIGN: Retrospective cohort. SETTINGS: Tertiary rehabilitation center, Riyadh, Saudi Arabia. PATIENTS AND METHODS: The study included all individuals who received medical treatment and tested positive for COVID-19 by means of RT-PCR during the period from March to September 2020. COVID-19 patients were classified using Edmonton Obesity Staging System (EOSS). MAIN OUTCOME MEASURES: COVID-19-related complications, including pneumonia and cytokine release syndrome, as well as the time length to COVID-19 negativization. SAMPLE SIZE: 315 patients. RESULTS: The median (25th-75th percentiles) age of the patients was 38 (31.5-49) years old. Males outnumbered females, and 66% of patients were non-Saudis. Forty-eight patients (15.2%) had obesity class I, whereas 13 patients (4.1%) had class II. Thirty-two patients (10.2%) were classified as EOSS stage 1, 105 patients (33.3%) were classified as EOSS stage 2, and 25 patients (7.9%) were assigned to EOSS stage 3. Males predominated in EOSS stages 1 and 2, whereas females predominated in stage 3. In EOSS stage 3, 52% of cases had moderate severity and 48% had severe illness. CONCLUSIONS: EOSS distinguishes the COVID-19 risks of poor outcomes beyond BMI. Patients who were overweight or obese but remained in the stage 1 of the EOSS had a lower risk of a poor COVID-19 outome than normal-weight patients. The health status of obese patients is a more precise indicator of the progression of COVID-19 during hospitalization than BMI alone. LIMITATIONS: Given the limited capacity of urgent care facilities to conduct a comprehensive evaluation of comorbidities and other relevant outcomes in all patients, it is plausible that certain patients may have been erroneously classified with an EOSS stage 2 diagnosis, when in fact they ought to have been assigned a stage 3 diagnosis.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".