BIOCHEMICAL MARKERS OF SEVERE COVID PATIENTS ADMITTED TO ICU IN A TERTIARY CARE HOSPITAL IN KERALA
Bibliographic record
Abstract
Background: COVID-19 has presented diverse clinical characteristics in different countries. Studies on the laboratory parameters of severe COVID-19 patients admitted to ICUs in India are limited. This study aims to investigate the laboratory ndings of patients with COVID-19 at different stages of the disease, focusing on the differences between survivors and non-survivors among severe COVID-19 patients admitted to the ICU in Kerala, India.Materials and Methods: This cross-sectional study analyzed medical records of patients admitted to the ICU of Chazhikattu Memorial Hospital in Thodupuzha, India, from January 2021 to June 2022. The hospital was designated as a COVID Hospital by the Government of Kerala during the early phase of the pandemic. Patients who met the inclusion criteria were selected, and the study was approved by the Hospital Ethics Committee. Results: Signicant differences were observed between survivors and non-survivors in terms of age, absolute neutrophil count (ANC), absolute lymphocyte count (ALC), neutrophil-to-lymphocyte ratio (NLR), urea, creatinine,D-Dimer and C-reactive protein (CRP) levels. Elevated D-dimer and CRP levels at admission, as well as persistently high ANC, low ALC, increased NLR, and high CRP levels during hospitalization, were signicantly associated with a higher risk of mortality. Conclusion: The study highlights the importance of certain laboratory parameters in predicting the clinical outcome of patients with severe COVID-19. These ndings may help guide the clinical management and risk stratication of patients with COVID-19, ultimately contributing to improved patient care and outcomes.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".