Treatment Patterns of Tocilizumab Utilization for Progressive Respiratory Distress during the COVID-19 Pandemic
Bibliographic record
Abstract
Purpose: This study’s objective was to describe treatment patterns of patients receiving then experimental drug tocilizumab for severe respiratory illness. Methodology: It is a retrospective case series of patients receiving tocilizumab for COVID-19 at a 380-bed hospital between 03/01/202 and 05/31/2020. Treatment patterns for tocilizumab for this series of ICU patients was modeled using a Spearman rho correlation for ranked associations. Results: There was significant variation in frequency and serial testing of inflammatory markers. There was no correlation between tocilizumab initiation and worsening respiratory status (r=0.19, p=.48) or between days since dosing and survival (R= -0.02, p= .95). No clear pattern emerged from tocilizumab administration during the pandemic. Conclusion: Protocols for untested new treatments are needed to overcome the uncertainty physicians face during pandemics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".