Randomized controlled trial of the effect of regular paracetamol on influenza infection
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Anti-pyretic treatment is recommended in the management of influenza infection. In animal models anti-pyretic treatment increases mortality from influenza. We investigated the effects of paracetamol on viral and clinical outcomes in adults with influenza infection. METHODS: This is a randomized, double-blind, placebo-controlled trial of adults aged 18-65 years with influenza-like illness and positive influenza rapid antigen test. Treatments were 1 g paracetamol four times a day, or matching placebo, for 5 days. Pernasal swabs were taken for influenza quantitative RT-PCR at Baseline and Days 1, 2 and 5. Temperature and symptom scores were recorded for 5-14 days or time of resolution respectively. The primary outcome variable was area under the curve (AUC) for quantitative PCR log10 viral load from Baseline to Day 5. RESULTS: A total of 80 participants were randomized: no one was lost to follow up, and one withdrew after 4 days. There were 22 and 24 participants who were influenza PCR-positive in placebo and in paracetamol groups respectively. Mean (SD) AUC PCR log10 viral load was 4.40 (0.91) in placebo and 4.64 (0.88) in paracetamol; difference was -0.24, 95% CI: -0.78 to 0.29, P = 0.36. In all participants there were no differences in symptom scores, temperature, time to resolution of illness and health status, with no interaction between randomized treatment and whether influenza was detected by PCR. CONCLUSION: Regular paracetamol had no effect on viral shedding, temperature or clinical symptoms in patients with PCR-confirmed influenza. There remains an insufficient evidence base for paracetamol use in influenza infection. CLINICAL TRIAL REGISTRATION: ACTRN12611000497909 at the Australian New Zealand Clinical Trials Registry.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".