A systematic review and meta‐analysis of interventions to delabel low‐risk penicillin allergies with consideration for sex and gender
Bibliographic record
Abstract
Abstract Aims Sex and gender may influence penicillin allergy label (PAL) prevalence and outcomes. This review evaluates the effectiveness and safety of direct delabelling (DD) and oral challenge (OC) for low‐risk patients and examines sex and gender differences in reporting and outcomes. Methods We searched PubMed, Database of Abstracts of Reviews and Effects, ClinicalTrials.gov , Cochrane Database of Systematic Reviews, International Pharmaceutical Abstracts, medRxiv, Ovid MEDLINE, and Ovid EMBASE until February 2024 for studies including DD or OC compared to no intervention, skin testing or other methods. Two reviewers assessed quality. Meta‐analyses were conducted, and subgroup analyses were carried out if I 2 > 75%. Descriptive data was analysed using NVivo 14 and reported narratively. Results From 1046 screened studies, 28 met inclusion criteria (two RCTs, 26 quasi‐experimental studies). Sex at baseline was reported in 86% of studies, with 61% females: 18% disaggregated outcomes by sex with a female mean delabelling rate of 66%. Gender variables were not reported. OC was not found to be more or less as effective comparaed to skin testing in RCTs (risk ratio [RR] 1.04; 95% confidence interval [CI] 0.95, 1.13, I 2 = 74%). DD interventions had a 27% delabelling rate (95% CI 10%, 50%, I 2 = 96%), with nursing staff achieving 29% (95% CI 15%, 47%, I 2 = 63%) and allergists/immunologists 6% (95% CI 0.00, 0.00, I 2 = 20%). Quasi‐experimental studies reported 90% delabelling for OC, with 59% by allergists/immunologists and 90% by pharmacists. Adverse events averaged 4% and were non‐severe. Conclusions DD and OC are effective for delabelling low‐risk penicillin allergies. Comprehensive data is lacking on sex and gender differences, indicating a need for further research.
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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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.035 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".