Outpatient Therapies for COVID-19: How Do We Choose?
Bibliographic record
Abstract
BACKGROUND: Several outpatient coronavirus disease 2019 (COVID-19) therapies have reduced hospitalization in randomized controlled trials. The choice of therapy may depend on drug efficacy, toxicity, pricing, availability, and available infrastructure. To facilitate comparative decision-making, we evaluated the efficacy of each treatment in clinical trials and estimated the cost per hospitalization prevented. METHODS: Wherever possible, we obtained relative risk for hospitalization from published randomized controlled trials. Otherwise, we extracted data from press releases, conference abstracts, government submissions, or preprints. If there was >1 study, the results were meta-analyzed. Using relative risk, we estimated the number needed to treat (NNT), assuming a baseline hospitalization risk of 5%, and compared the cost per hospitalization prevented with the estimate for an average Medicare COVID-19 hospitalization ($21 752). Drug pricing was estimated from GoodRx, from government purchases, or manufacturer estimates. Administrative and societal costs were not included. Results will be updated online as new studies emerge and/or final numbers become available. RESULTS: At a 5% risk of hospitalization, the estimated NNT was 80 for fluvoxamine, 91 for colchicine, 72 for inhaled corticosteroids, 24 for nirmatrelvir/ritonavir, 50 for molnupiravir, 28 for remdesivir, 25 for sotrovimab, 29 for casirivimab/imdevimab, and 29 for bamlanivimab/etesevimab. For drug cost per hospitalization prevented, colchicine, fluvoxamine, inhaled corticosteroids, and nirmatrelvir/ritonavir were below the Medicare estimated hospitalization cost. CONCLUSIONS: Many countries are fortunate to have access to several effective outpatient therapies to prevent COVID-19 hospitalization. Given differences in efficacy, toxicity, cost, and administration complexity, this assessment serves as one means to frame treatment selection.
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 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 | 0.064 | 0.227 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".