Remdesivir for severe covid-19: a clinical practice guideline
Bibliographic record
Abstract
CLINICAL QUESTION: on 22 May 2020. CURRENT PRACTICE: Remdesivir has received worldwide attention as a potentially effective treatment for severe covid-19. After rapid market approval in the US, remdesivir is already being used in clinical practice. RECOMMENDATIONS: The guideline panel makes a weak recommendation for the use of remdesivir in severe covid-19 while recommending continuation of active enrolment of patients into ongoing randomised controlled trials examining remdesivir. HOW THIS GUIDELINE WAS CREATED: An international panel of patients, clinicians, and methodologists produced these recommendations in adherence with standards for trustworthy guidelines using the GRADE approach. The recommendations are based on a linked systematic review and network meta-analysis. The panel considered an individual patient perspective and allowed contextual factors (such as resources) to be taken into account for countries and healthcare systems. THE EVIDENCE: The linked systematic review (published 31 Jul 2020) identified two randomised trials with 1300 participants, showing low certainty evidence that remdesivir may be effective in reducing time to clinical improvement and may decrease mortality in patients with severe covid-19. Remdesivir probably has no important effect on need for invasive mechanical ventilation. Remdesivir may have little or no effect on hospital length of stay. UNDERSTANDING THE RECOMMENDATION: Most patients with severe covid-19 would likely choose treatment with remdesivir given the potential reduction in time to clinical improvement. However, given the low certainty evidence for critical outcomes and the fact that different perspectives, values, and preferences may alter decisions regarding remdesivir, the panel issued a weak recommendation with strong support for continued recruitment in randomised trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.827 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".