Bibliographic record
Abstract
In children, as in adults, H1-antagonists are useful in the treatment of allergic rhinoconjunctivitis. Level 1 evidence for their efficacy in this disorder has been obtained in many well-designed pediatric studies. The widespread use of H1-antagonists in upper respiratory tract infections or otitis media in children is not supported by a strong scientific rationale. H1-antagonists are not harmful in children with asthma and, indeed, may have some beneficial effects in children with mild asthma. Their role in delaying or preventing asthma from developing in high-risk infants and toddlers is currently an important area of clinical investigation. The evidence base for their use in children with urticaria or atopic dermatitis still contains large gaps. First-generation H1-antagonists are presumed to be safe for use in infants and children. While they have undoubtedly been administered without apparent harm to millions in this age group, they impair CNS function far more commonly than is generally realized. Their use should be restricted to two uncommon situations: children with urticaria or atopic dermatitis whose pruritus is so severe that the sedation produced by an old H1-antagonist, such as hydroxyzine, is a benefit rather than a risk; and children with anaphylaxis who require intravenous diphenhydramine as adjunctive treatment to epinephrine and other modalities. Apart from these exceptions, in patients of all ages, second-generation H1-antagonists free from CNS adverse effects are clearly the medications of choice. Pediatric formulations of the new H1-antagonists cetirizine, fexofenadine, and loratadine are now available for use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".