Adverse reactions to β-lactam antimicrobials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Beta-lactam antibiotics are among the most clinically useful antimicrobials used in medicine. Unfortunately, adverse events related to their use remain poorly understood by many clinicians and, in particular, the misdiagnosis of β-lactam allergy and misunderstanding of crossreactivity among members of the β-lactam antibiotics may effectively eliminate a whole class of antimicrobials from use and require the use of broader spectrum agents with less well-established safety profiles. AREAS COVERED: This review describes the range, diagnosis and management of adverse events associated with β-lactam antimicrobials, particularly focusing on recognition, diagnosis and management of true allergy and risk of cross-sensitivity between β-lactam antibiotics. A literature review was undertaken using PubMed, focusing primarily on literature published in the past 10 years relating to β-lactam adverse events and allergy. EXPERT OPINION: Beta-lactams are generally safe drugs and serious adverse events are rare and allergy is overdiagnosed. Accurate diagnosis can usually be achieved through careful history and in some instances skin or in vitro testing is required. Even among individuals with true immediate-type allergy to penicillin, most cephalosporins are readily tolerated and desensitization is usually an option in cases where no alternate antimicrobials are available. Other allergic reactions (Type II, III and IV) are rare and avoidance of the culprit agent is generally recommended. Nonallergic or morbilliform rashes are generally not allergic in nature and should not prompt drug or class avoidance. Other adverse events are frequently dose-related and can be avoided by appropriate dosing and consideration of renal function.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.010 |
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 it