Fluvoxamine for the Early Treatment of SARS-CoV-2 Infection: A Review of Current Evidence
Bibliographic record
Abstract
SARS-CoV-2 infection causes COVID-19, which frequently leads to clinical deterioration and/or long-lasting morbidity. Academic and governmental experts throughout the USA met in 2021 to discuss the potential for use of fluvoxamine as early treatment of SARS-CoV-2 infection. Fluvoxamine is a selective serotonin reuptake inhibitor (SSRI) that is a strong sigma-1 receptor agonist, and this may effectively reduce cytokine production, preventing clinical deterioration. This repurposed psychiatric medication has a well-known safety record, is inexpensive, easy to use, and widely available, all of which are advantages during this global COVID-19 pandemic. At the meeting, experts reviewed the existing published literature on the use of fluvoxamine as experimental COVID-19 treatment, as well as prior research on the potential mechanisms for anti-inflammatory effects of fluvoxamine, including for other conditions including sepsis. Investigators shared current trials underway and existing gaps in knowledge. Two randomized controlled trials and one observational study examining the effect of fluvoxamine in COVID-19 treatment have found high efficacy. Four larger randomized clinical trials are currently underway, including three in the USA and Canada. More data are needed on dosing and mechanisms of effect; however, fluvoxamine appears to have substantial potential as a safe and widely available medication that could be repurposed to ameliorate serious COVID-19-related morbidity and mortality. As of April 2021, fluvoxamine was mentioned in the NIH COVID-19 treatment guidelines, although no recommendation is made for or against use. Available data may warrant clinician discussion of fluvoxamine as a treatment option for COVID-19, using shared decision making. Video Abstract.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".