Diagnostic challenges, the evaluation of antibiotic allergy
Bibliographic record
Abstract
PURPOSE OF REVIEW: Antibiotic allergy is commonly reported and often challenging to evaluate. This review highlights recent developments in our understanding of antibiotic allergy, primarily surrounding evaluation, and diagnosis of antibiotic allergy. We provide historical context to establish a framework, lending relevance to the latest studies. RECENT FINDINGS: Clinicians have typically employed skin testing as a first step in the diagnosis of drug allergy, reserving drug provocation challenge, the gold standard for diagnosis, for those with negative skin tests. Although skin tests have a good negative predictive value, the positive predictive value has never been established. An increasing amount of research is demonstrating that drug provocation challenge is well tolerated as the initial evaluation in patients with non-life-threatening reactions to antibiotics. This research also calls into question the value of skin testing in these patients. SUMMARY: Skin testing has long been used as the initial investigation in the diagnosis of drug allergy. New research supports that this may not be necessary in all patients, particularly those with non-life-threatening reactions. Further research into the validity of skin testing is required, along with the development of new diagnostic tests for antibiotic 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".