Association of body mass index with COVID-19-related neurologic sequelae: a retrospective cohort study
Bibliographic record
Abstract
We sought to explore the relationship between body mass index (BMI) and neurologic outcomes following acute COVID-19 infection. We conducted a retrospective electronic medical record-based cohort study enrolling adults with laboratory-confirmed acute COVID-19 infection who presented to 1 of 12 academic and community hospitals in Southwestern Ontario, Canada between April 1, 2020 and July 31, 2021. Primary subjective (anosmia, dysgeusia, and/or headache) and objective (aseptic meningitis, ataxia, delirium, encephalopathy, encephalitis, intracranial hemorrhage, ischemic stroke, and/or seizure) composite neurologic outcomes were assessed, comparing obese and overweight individuals to those with underweight/normal BMI indices, adjusting for baseline characteristics. Secondary outcomes (severity of illness, length of hospital stay, SARS-CoV-2 viral load, mortality) were similarly analyzed. A total of 1437 enrolled individuals, of whom 307 (21%), 456 (32%), and 674 (47%) were underweight/normal, overweight, and obese, respectively. On multivariable analysis, there was no association between BMI category and the composite outcome for subjective (odds ratio [OR] 1.17, 95% CI 0.84-1.64, Bonferroni p = 1.00 for obese; OR 1.02, 95% CI 0.70-1.48; Bonferroni p = 1.00 for overweight) and objective (OR 0.74, 95% CI 0.42-1.30, p = 0.29 for obese; OR = 0.80, 95% CI 0.45-1.43, p = 0.45 for overweight) neurologic manifestations. There was no association between BMI category and any secondary outcome measure and no evidence of effect modification by age or sex. This study demonstrates the absence of an association between BMI and neurologic manifestations following acute COVID-19 illness. Prospective studies using standardized data collection tools and direct measures of body fat are warranted to obtain more valid effect estimates.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".