Remdesivir-related cost-effectiveness and cost and resource use evidence in COVID-19: a systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has been a global health emergency since December 2019, leading to millions of deaths worldwide and placing significant pressures, including economic burden, on individual patients and healthcare systems. As of February 2022, remdesivir is the only US Food and Drug Administration (FDA)-approved treatment for severe COVID-19. This systematic literature review (SLR) aimed to summarise economic evaluations, and cost and resource use (CRU) evidence related to remdesivir during the COVID-19 pandemic. METHODS: Searches of MEDLINE, Embase the International Health Technology Assessment (HTA) database, reference lists, congresses and grey literature were performed in May 2021. Articles were reviewed for relevance against pre-specified criteria by two independent reviewers and study quality was assessed using published checklists. RESULTS: Eight studies reported resource use and five reported costs related to remdesivir. Over time, the prescription rate of remdesivir increased and time from disease onset to remdesivir initiation decreased. Remdesivir was associated with a 6% to 21.3% decrease in bed occupancy. Cost estimates for remdesivir ranged widely, from $10 to $780 for a 10-day course. In three out of four included economic evaluations, remdesivir treatment scenarios were cost-effective, ranging from ~ 8 to ~ 23% of the willingness-to-pay threshold for the respective country. CONCLUSIONS: Economic evidence relating to remdesivir should be interpreted with consideration of the broader clinical context, including patients' characteristics and the timing of its administration. Nonetheless, remdesivir remains an important option for physicians in aiming to provide optimal care and relieve pressure on healthcare systems through shifting phases of the pandemic.
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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 | 0.010 | 0.362 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it