Early clinical experience using tecovirimat during the 2022 mpox epidemic in Toronto underscores ongoing clinical equipoise and the need for randomized trials
Bibliographic record
Abstract
BACKGROUND: Tecovirimat is an antiviral drug that was used compassionately for treating mpox in high-income settings during the 2022 global outbreak. Randomized controlled trials of its efficacy have not yet been completed. OBJECTIVES: To describe medication adherence, tolerability and clinical outcomes of adults receiving open-label tecovirimat for mpox infection. METHODS: We conducted a prospective observational study and a retrospective case series of adults with mpox cared for at three academic hospitals in Toronto, Canada, between May and August 2022. We present a descriptive analysis of those prescribed oral tecovirimat 600 mg twice daily for 14 days for the management of severe manifestations. RESULTS: Of 69 consenting participants, all were cisgender men, of whom 60 (87%) identified as gay, and 6 (9%) as bisexual. Nearly half (46%) were living with HIV, with a median (IQR) CD4 count of 468 (328-678) cells/mm3, among whom plasma HIV RNA was <20 copies/mL in 29 (91%) participants. One-third (33%) of participants received tecovirimat during the course of their illness. All participants experienced a decline in number of symptoms over time, but three treated participants initially experienced worsening symptoms despite therapy. Self-reported adherence to tecovirimat was excellent and tolerability was good. CONCLUSIONS: Our experience prescribing tecovirimat for mpox suggests it is safe and well tolerated, but the evolution of symptoms in some tecovirimat-treated patients underscores the ongoing uncertainty regarding its efficacy. In the context of considerable community demand for the drug, efforts should be made to connect mpox patients to rigorous randomized controlled trials, given this ongoing clinical equipoise.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".