Effectiveness of early-treatment interventions on self-reported long COVID: A multi-arm, multi-stage adaptive platform control trial
Bibliographic record
Abstract
Abstract Approximately 20% of people infected with COVID-19 develop at least one persistent condition potentially attributable to their SARS-CoV-2 infection. We sought to determine the effectiveness of early COVID-19 treatment interventions on long COVID symptoms. We conducted a multi-arm multi-stage adaptive platform trial at 12 public health clinics in Brazil between June 2020 and July 2022. Participants were followed for 60. Patients received one of six interventions (doxazosin, fluvoxamine, fluvoxamine in combination with inhaled budesonide, interferon-lambda, ivermectin, or metformin) or matching placebo. The primary outcome was persistence of COVID-19 symptoms at 60 days after randomization. We analyzed data from 5,700 participants across study cohorts. Overall, approximately 22% of patients reported at least one ongoing symptom 60 days after randomization, regardless of the early treatment they received. At day 60, we did not find any statistical benefit of any intervention on recovery, cure fractions, or PROMIS scores (mental and physical).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".