Fluvoxamine for the Early Treatment of SARS-CoV-2 Infection: A Review of Current Evidence
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
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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