Misconceptions Surrounding Penicillin Allergy: Implications for Anesthesiologists
Bibliographic record
Abstract
Administration of preoperative antimicrobial prophylaxis, often with a cephalosporin, is the mainstay of surgical site infection prevention guidelines. Unfortunately, due to prevalent misconceptions, patients labeled as having a penicillin allergy often receive alternate and less-effective antibiotics, placing them at risk of a variety of adverse effects including increased morbidity and higher risk of surgical site infection. The perioperative physician should ascertain the nature of previous reactions to aid in determining the probability of the prevalence of a true allergy. Penicillin allergy testing may be performed but may not be feasible in the perioperative setting. Current evidence on the structural determinants of penicillin and cephalosporin allergies refutes the misconception of cross-reactivity between penicillins and cefazolin, and there is no clear evidence of an increased risk of anaphylaxis in cefazolin-naive, penicillin-allergic patients. A clinical practice algorithm for the perioperative evaluation and management of patients reporting a history of penicillin allergy is presented, concluding that cephalosporins can be safely administered to a majority of such patients.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".