Plasma zinc status and hyperinflammatory syndrome in hospitalized COVID-19 patients: an observational study
Bibliographic record
Abstract
Abstract Deficiency of the element zinc is associated with cytokine releasing syndrome (CRS) and the related acute respiratory distress syndrome as well as impaired antiviral response. Similar complications associate with severe SARS-CoV-2. We conducted a prospective, single-center, observational study in a tertiary university hospital (CUB-Hopital Erasme, Brussels) to address the zinc status, the association between the plasma zinc concentration, development of CRS, and the clinical outcomes in PCR-confirmed and hospitalized COVID-19 patients. One hundred and thirty-nine eligible patients were included between May 2020 and November 2020 (median age of 65 years [IQR, 54 to 77]). Our cohort’s mean plasma zinc concentration was 56.2 µg/dL (standard deviation [SD], 14.8) compared to 75.7 µg/dL (SD = 18.9 µg/dL) in the retrospective non-COVID-19 control group (N = 1513; P <.001). Markedly, the absolute majority of patients (96%) were zinc deficient (<80 µg/dL). The mean zinc concentration was lower in patients with CRS compared to those without CRS (−5 µg/dL; 95% CI, -10.5 to 0.051; P = 0.048). Among the tested outcomes, zinc concentration is significantly correlated with only the length of hospital stay (rho = -0.19; P = 0.022), but not with mortality or morbidity. As such, our findings do not support the role of zinc as a robust prognostic marker among hospitalized COVID-19 patients who in our cohort presented high prevalence of zinc deficiency. It might be more beneficial to explore the role of zinc as a biomarker for assessing the risk of developing a tissue-damaging CRS and predicting outcomes in patients diagnosed with COVID-19 at the early stage of the disease.
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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) | 0.001 | 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 | 0.001 |
| 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".