Relationship Between Admission Body Mass Index (BMI), Nutritional-Inflammatory Biomarkers, and In-Hospital Outcomes among Hospitalized COVID-19 Patients
Bibliographic record
Abstract
Background: COVID-19 continues to present challenges, as certain patients require hospitalization due to pre-existing risk factors or the severity of the illness, possibly leading to adverse clinical outcomes. This thesis aims to investigate the association between BMI categories, biomarkers and in-hospital outcomes.Methods: This retrospective study analyzed data from 241 hospitalized COVID-19 patients from March 2020 to May 2022 to examine the association of patient characteristics, including BMI categories and patient outcomes. Results: Obesity was independently associated with decreased in-hospital mortality after adjusting for potential confounders. Obese patients were predominantly female, younger in age, with elevated admission levels of albumin. Moreover, a higher decrease in albumin level in COVID-19 patients was linked to prolonged hospital stays. Also, deceased patients showed significantly elevated Neutrophil-to-Lymphocyte Ratio. Conclusion: BMI, along with albumin at admission and Neutrophil-to-Lymphocyte Ratio, might serve as indicative factors for assessing the prognosis and morbidity of hospitalized COVID-19 patients.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".