The Value of Skin Testing for Penicillin Allergy in an Inpatient Population: Analysis of the Subsequent Patient Management
Bibliographic record
Abstract
It was decided to assess the value of skin testing in a group of inpatients with a remote history of penicillin allergy, in terms of whether or not beta-lactams were subsequently given, if any adverse reactions occurred as a result of this therapy, and if labeling of the patient record was changed subsequent to skin testing and/or challenge. All patients seen in consultation with a history of penicillin allergy were assessed. When done, skin tests were performed with the major and minor determinants of penicillin and semisynthetic penicillins. Charts were reviewed after discharge in terms of the antibiotics given during admission, adverse events, and the medical record and hospital database labeling for drug allergy at discharge. Skin testing was carried out in 79% of 67 patients assessed and in all, the tests were negative. Beta-lactam therapy was recommended in 51/53 patients but was given in only 57% of these cases. At discharge, 49% of patients' records still carried the penicillin allergy label, despite negative skin testing and/or successful completion of a course of beta-lactam therapy. So, in approximately half of the patients reviewed, beta-lactams were not given despite negative skin tests and a recommendation to do so, if indicated, and 49% of patients were still inappropriately labeled as being penicillin-allergic on discharge.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".