An investigation into the correlation of vitamin D status and management outcomes in patients with severe COVID-19 at a South African tertiary hospital
Bibliographic record
Abstract
Background: Severe COVID-19 has a poor prognosis, and biomarkers may predict disease severity. This study aimed to assess the effect of baseline Vitamin D (VitD) inadequacy on outcome of patients with severe COVID-19 admitted to intensive care unit (ICU) in a tertiary hospital in South Africa. Methods: Patients with confirmed SARS-CoV-2 were recruited during wave II of the pandemic in Cape Town. Eighty-six patients were included in the study. They were categorized into three groups "VitD deficient, VitD insufficient and VitD sufficient". We combined the VitD deficient with insufficient group to form "VitD inadequate'' group. Cox regression analysis was done to assess the association between VitD status and mortality. Factors with p< 0.05 in adjusted multivariable cox regression were considered statistically significant. Results: The proportion of VitD inadequacy was 64% (55/86), with significantly higher proportion of hypertension (66%; p 0.012). Kaplan Meir curve showed no significant difference in the probability of survival among the COVID-19 patients admitted in the ICU with or without VitD inadequacy. However, patients with elevated serum creatinine were significantly more at risk of dying (Adjusted Hazard Ratio 1.008 (1.002 - 1.030, p<0.017). Conclusion: Our study found a high prevalence of VitD inadequacy (combined deficiency and insufficiency) in COVID-19 patients admitted to the ICU. This may indicate a possible risk of severe disease. Whilst there was no statistically significant relationship between VitD status and mortality in this cohort, baseline VitD may be an important prognostic biomarker in COVID-19 patients admitted to the ICU, particularly in those with comorbidities that predispose to VitD deficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".