Amoxicillin Allergy: Old Concepts, New Concepts and Change of Concepts
Bibliographic record
Abstract
More than one million Canadian children are treated annually with antibiotics, mainly amoxicillin.1-4 Up to 10% of children develop rashes while treated with amoxicillin.1-5 The majority of children presenting with rashes during amoxicillin treatment are diagnosed with amoxicillin hypersensitivity without further evaluation and often carry this diagnosis into adulthood. There remains controversy in the medical literature regarding the most accurate and safe strategy for diagnosing amoxicillin hypersensitivity. As a result, most children continue to avoid amoxicillin and other penicillin derivatives throughout life in favor of alternatives that are reported to be less effective, more toxic, and more expensive. There is much we do not know about the pathogenesis of amoxicillin hypersensitivity. Consequently, the appropriate diagnostic strategy required to establish the presence of true amoxicillin hypersensitivity is unclear. In order to develop an appropriate diagnostic approach, it is important to understand the pathogenic mechanisms accounting for amoxicillin hypersensitivity and the validity of the available confirmatory tests. This review will discuss the pathogenic mechanisms underlying amoxicillin allergy, describe the challenges in the diagnosis of amoxicillin allergy, critically assess the role of skin testing and IgE levels and discuss the appropriate diagnostic strategy in individuals presenting with suspected amoxicillin allergy.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".