Hypersensitivity reactions to biologics in children
Bibliographic record
Abstract
INTRODUCTION: Hypersensitivity reactions (HSRs) have been observed with the use of biologics in children. The management of HSRs in children is mainly based on experiences from the adult population. Recently, data from different centers experienced in managing these reactions, including desensitization in children, have been published, allowing clinicians to have an appropriate global overview and compare results. AREAS COVERED: This review highlights the published data on hypersensitivity reactions to biologics in children and drug desensitization protocols adapted to the pediatric population. EXPERT OPINION: With regard to HSRs to biologics in children, few data are available. Compared with the adult population, there is a lack of knowledge in the endophenotypes, management and the standardization of protocols including premedication regimens in children. An international consensus is needed to provide clinicians with new insight on how to apply personalized management and to perform tailored desensitization protocols in pediatric populations. Various specialists including allergists, pediatricians, oncologists, hematologists, rheumatologists, and pharmacists, should build a multidisciplinary management team to keep pediatric patients on their best treatment options in the safest manner.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".