Point-of-Care β-Lactam Allergy Skin Testing by Antimicrobial Stewardship Programs: A Pragmatic Multicenter Prospective Evaluation
Bibliographic record
Abstract
Background: β-lactam allergy skin testing (BLAST) is recommended by antimicrobial stewardship program (ASP) guidelines, yet few studies have systematically evaluated its impact when delivered at point of care. Methods: We conducted a pragmatic multicenter prospective evaluation of the use of point-of-care BLAST by ASPs. In staggered 3-month intervals, ASP teams at 3 hospitals received training by allergists to offer BLAST for eligible patients with infectious diseases receiving nonpreferred therapy due to severity of their reported allergy. The primary outcome was the proportion of patients receiving the preferred β-lactam therapy. Results: Of 827 patients with reported β-lactam allergy over 15 months, β-lactam therapy was preferred among 632 (76%). During baseline periods, 50% (124/246) received preferred β-lactam therapy based on history, compared with 60% (232/386) during the intervention periods (P = .02), which improved further to 81% (313/386) upon provision of BLAST (P < .001) without any increase in incidence of adverse drug reactions (4% vs 3%; P = .4). After adjusting for patient variables and the correlation between hospitals, the intervention period was associated with a 4.5-fold greater odds of receiving preferred β-lactam therapy (95% confidence interval, 2.4-8.2; P < .0001). Conclusions: The use of BLAST at the point of care across 3 hospital ASPs resulted in greater use of preferred β-lactam therapy without increasing the risk of adverse drug reactions. Longer-term studies are needed to better assess the safety and clinical impact of this ASP intervention.
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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 | 0.042 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".