RCT Abstract - Paracetamol compared to ibuprofen as required for pain or fever: One-year outcomes for eczema and bronchiolitis in the PIPPA Tamariki RCT
Bibliographic record
Abstract
Background In non-experimental studies early-life exposure to paracetamol is associated with increased risks of childhood eczema and wheeze. Objective To compare paracetamol to ibuprofen as required for fever and pain in the first year, for the incidence of eczema and bronchiolitis at one year of age. Methods Multicentre, open-label, two-arm, parallel-groups, superiority RCT randomising infants aged <8 weeks of age, stratified by recruitment site, maternal asthma status and multiple birth, to only paracetamol (15mg/kg) or only ibuprofen (<3 months: 5mg/kg; ≥3 months: 10mg/kg), as required for fever and pain, until one year of age. Eczema was defined by UK Diagnostic Criteria or eczema hospitalisation. Bronchiolitis was defined by at least one relevant hospitalisation. Risk differences (RD) and odds ratios (OR), were estimated. The latter adjusted for cluster randomisation and stratification variables. Results From April 2018 to July 2023, 3923 infants were enrolled and 3908 infants had analysis data. Eczema occurred in 322/1985 (16.2%) in the paracetamol, and 296/1923 (15.4%) in the ibuprofen group: RD (95% CI) 0.8% (-1.5 to 3.1), P=0.48. The adjusted OR (95% CI) was 1.10 (0.92 to 1.32), P=0.29. Bronchiolitis occurred in 98/1985 (4.9%) in the paracetamol, and in 82/1923 (4.3%) in the ibuprofen group: RD (95% CI) 0.7% (-0.6 to 2.0), P=0.32. The adjusted OR (95% CI) was 1.17 (0.75 to 1.85), P=0.21. 17 participants had serious adverse events (paracetamol 8; ibuprofen 9); none were thought to be due to the study drug. Conclusion There was no evidence of a difference in eczema or bronchiolitis at age one year comparing paracetamol with ibuprofen.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".