Introduction of a Practice Guideline for Penicillin Skin Testing Improves the Appropriateness of Antibiotic Therapy
Bibliographic record
Abstract
We hypothesized that the introduction of a practice guideline for penicillin skin testing would increase the appropriateness of skin testing and reduce antibiotic costs for patients with a history of penicillin allergy who have infections caused by penicillin-susceptible pathogens. We measured the appropriateness of skin testing and daily antibiotic costs before and after the introduction of a guideline for penicillin skin testing. For patients who had negative results of skin testing and were subsequently treated with a penicillin instead of an alternative antibiotic, we calculated the difference between the actual costs and the projected costs of continuing alternative antibiotics without skin testing. After the guideline was introduced, appropriateness of skin testing increased from 17% to 64%, but daily antibiotic costs did not change. For patients who had negative results of skin testing and who were subsequently treated with a penicillin, there was no difference between actual costs and the projected costs if they had not been skin tested. We conclude that introduction of a guideline for penicillin skin testing increases the percentage of eligible patients who have a skin test, and it does so without increasing costs.
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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 | 0.013 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".