Effect of prophylactic or therapeutic administration of paracetamol on immune response to DTwP-HepB-Hib combination vaccine in Indian infants
Bibliographic record
Abstract
BACKGROUND: Vaccination is considered as the most cost effective method for preventing infectious diseases. Low grade fever is a known adverse effect of vaccination. In India, it is a common clinical practice to prescribe paracetamol either prophylactically or therapeutically to manage fever. Some studies have shown that paracetamol interferes with antibody responses following immunization. This manuscript reports the outcome of a post hoc analysis of data from a clinical trial of a pentavalent vaccine in Indian infants where paracetamol was not used or was used either as prophylaxis or for treatment of fever. METHODS: Pre and post vaccine antibody levels against Diphtheria, Tetanus, Pertussis, Hepatitis B, Haemophilus influenzae type B were assessed in no paracetamol and paracetamol groups. The paracetamol group was further divided into prophylactic and treatment groups. RESULTS: Similar rates of seroprotection/seroresponse for anti-D, anti-T, anti-wP, anti-PT, anti-HBs and anti-PRP were observed in all the groups. There was no clear tendency for difference in percentage seroprotection/seroresponse and geometric mean (GM) titers in any of the groups. CONCLUSION: The study found no evidence that paracetamol usage either as prophylactic or for treatment impact immunological responses to DTwP-HepB-Hib combination vaccine. [Clinical trial registry of India (study registration number CTRI/2012/08/002872)].
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".