The Impact of Infliximab on Hyperinflammation State in Hospitalized COVID-19 Patients: A Retrospective Study
Bibliographic record
Abstract
Background and Objectives: Elevated levels of pro-inflammatory cytokines have been linked to increased mortality in COVID-19 patients. Infliximab, a tumor necrosis factor inhibitor, has been reported to improve outcomes in COVID-19 patients by targeting the hyperinflammatory response. Our objective was to evaluate the effectiveness of incorporating Infliximab into standard care guidelines for the management of COVID-19. Materials and Methods: A retrospective analysis was conducted on 111 participants who were moderate to severe COVID-19 patients admitted to the hospital. Among them, 74 individuals received solely standard treatment, while 37 received standard therapy plus Infliximab. The primary outcomes of the study centered around the changes in laboratory test parameters. The secondary clinical findings included clinical recovery defined as improvement in patient oxygenation, time till recovery, and assessing necessity for ICU admission, and mortality rates. Results: There was no statistical difference observed in the inflammatory markers including, LDH, Ferritin, CRP, neutrophil to lymphocyte ratio (NLR), and P/F ratio between both groups and in the clinical outcomes including clinical recovery (p = 1.0), time to improvement (p = 0.436), and mortality rate (p = 0.601). However, there was a significant increase in secondary infection (45.9%, 20.3%; p = 0.005), and in liver enzymes, ALT (79.5, 50.0 IU/L; p = 0.02) and AST (57.5, 38.0 IU/L; p = 0.019) in the Infliximab group and the standard care group, respectively. Conclusions: Infliximab therapy did not demonstrate significant benefits compared to standard of care in moderate to severe 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".