Reduced fixed dose tocilizumab 400 mg IV compared to weight-based dosing in critically ill patients with COVID-19: A before-after cohort study
Bibliographic record
Abstract
Background: Interleukin-6 inhibitors reduce mortality in severe COVID-19. British Columbia began using tocilizumab 8 mg/kg (maximum 800 mg) in January 2021 in critically ill patients with COVID-19, but due to drug shortages, decreased dosing to 400 mg IV fixed dose in April 2021. The aims of this study were twofold: to compare physiological responses and clinical outcomes of these two strategies, and examine the cost-effectiveness of treating all patients with 400 mg versus half the patients with 8 mg/kg and the other half without tocilizumab. Methods: This was a single-centre, before-after cohort study of critically ill COVID-19 patients treated with tocilizumab, and a control cohort treated with dexamethasone only. Physiological responses and clinical outcomes were compared between patients receiving both doses of tocilizumab and those receiving dexamethasone only. We built a decision tree model to examine cost-effectiveness. Findings: =7), with no significant difference in all pair-wise comparison. At an assumed willingness-to-pay threshold of $50,000 Canadian per life-year, utilizing 400 mg for all patients rather than 8 mg/kg for half the patients is cost-effective in 51.6% of 10,000 Monte Carlo simulations. Interpretation: Both doses of tocilizumab demonstrated comparable reduction of inflammation with similar 28-day mortality. Without consideration of equity, the net monetary benefits of providing 400 mg tocilizumab to all patients are comparable to 8 mg/kg to half the patients. In the context of ongoing drug shortages, fixed-dose 400 mg tocilizumab may be a practical, feasible and economical option. Funding: This work was supported by a gift donation from Hsu & Taylor Family to the VGH Foundation, and the Yale Bernard G. Forget Scholarship.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".