Beta-lactam allergy in the paediatric population
Bibliographic record
Abstract
Beta-lactam allergy is commonly diagnosed in paediatric patients, but over 90% of individuals reporting this allergy are able to tolerate the medications prescribed after evaluation by an allergist. Beta-lactam allergy labels are associated with negative clinical and administrative outcomes, including use of less desirable alternative antibiotics, longer hospitalizations, increasing antibiotic-resistant infections, and greater medical costs. Also, children with true IgE-mediated allergy to penicillin medications are often advised to avoid all beta-lactam antibiotics, including cephalosporins, which is likely unnecessary in greater than 97% of those reporting penicillin allergies. Most patients can be safely treated with penicillin or amoxicillin if they do not have a history compatible with IgE-mediated or systemic, delayed reactions such as Stevens-Johnson syndrome (SJS), serum sickness-like reactions, drug reaction with eosinophilia and systemic symptoms (DRESS) syndrome, or acute generalized exanthematous pustulosis (AGEP). Guidance is provided on how to stratify risk of beta-lactam allergy, and on test dosing and monitoring in the outpatient setting for patients deemed low risk. Guidance for patients at higher risk of beta-lactam allergy includes criteria for appropriate referral to allergists and the use of alternative antimicrobials, such as cephalosporins, while awaiting specialist assessment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".