The outcome of patients with Severe COVID 19 treated with tocilizumab:a Retrospective Cohort study
Bibliographic record
Abstract
BACKGROUND Coronavirus disease 2019 triggers a cytokine storm resulting in high mortality. A key player in this cytokine storm has been considered as interleukin-6. Tocilizumab, a monoclonal antibody, is theorized to treat COVID-19 by inhibiting the interleukin-6 receptor. However, conflicting data supporting this hypothesis is available in the literature. METHODOLOGY This retrospective cohort included 1001 severe COVID-19 patients from a tertiary care hospital, Pakistan, hospitalized from March 2020 to August 2021. Primary outcome was the proportion of patients expired. Beside proportion of patients discharged, requirements for assisted ventilation and ICU admission were other outcomes. We also saw the impact of tocilizumab on radiological findings, oxygenation, and inflammatory markers. RESULTS Of 100 patients in the toci group, 85(85%) patients were ultimately discharged, compared to only 57.9% patients in R-toci and 62.9% patients in SOC group were discharged (p < 0.001). Fifty-nine (38.8%) patients in R-toci group and one hundred ninety-six (26.2%) patients in SOC group were expired in comparison to toci group in which only eight (8%) of patients were expired (p < 0.001). Similarly need for invasive mechanical ventilation and ICU admission remained lowest in toci group. Improvement in radiological findings, oxygenation, and inflammatory markers was observed after using tocilizumab. Patients receiving standard of care had lowest survival. Among discharge patients, those who received standard of care had longest hospital stay. CONCLUSION Tocilizumab use in patients with severe COVID-19 is associated with a significant reduction in mortality, need for ICU admission and invasive mechanical ventilation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".