A randomized study of fever prophylaxis and the immunogenicity of routine pediatric vaccinations
Bibliographic record
Abstract
OBJECTIVE: Prophylactic antipyretic use during pediatric vaccination is common. This study assessed whether paracetamol or ibuprofen prophylaxis interfere with immune responses to the 13-valent pneumococcal conjugate vaccine (PCV13) given concomitantly with the combined DTaP/HBV/IPV/Hib vaccine. METHODS: Subjects received prophylactic paracetamol or ibuprofen at 0, 6-8, and 12-16 h after vaccination, or 6-8 and 12-16 h after vaccination at 2, 3, 4, and 12months of age. At 5 and 13months, immune responses were evaluated versus responses in controls who received no prophylaxis. RESULTS: After the infant series, paracetamol recipients had lower levels of circulating serotype-specific pneumococcal anticapsular immunoglobulin G than controls, reaching significance (P<0.0125) for 5 serotypes (serotypes 3, 4, 5, 6B, and 23F) when paracetamol was started at vaccination. Opsonophagocytic activity assay (OPA) results were similar between groups. Ibuprofen did not affect pneumococcal responses, but significantly (P<0.0125) reduced antibody responses to pertussis filamentous hemagglutinin and tetanus antigens after the infant series when started at vaccination. No differences were observed for any group after the toddler dose. CONCLUSIONS: Prophylactic antipyretics affect immune responses to vaccines; these effects vary depending on the vaccine, antipyretic agent, and time of administration. In infants, paracetamol may interfere with immune responses to pneumococcal antigens, and ibuprofen may reduce responses to pertussis and tetanus antigens. The use of antipyretics for fever prophylaxis during infant vaccination merits careful consideration. ClinicalTrials.gov identifier: NCT01392378https://clinicaltrials.gov/ct2/show/NCT01392378?term=NCT01392378&rank=1.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".