Global differences and risk factors influencing drug hypersensitivity quality of life: A multicenter, multiethnic study of drug allergy across 3 continents
Bibliographic record
Abstract
Background Penicillin allergy labels are associated with many adverse outcomes. Fear and restriction of future medication use also have an impact on health-related quality of life (HR-QoL). However, the impact of a drug allergy on HR-QoL and its associated factors remains unknown. Objective We sought to investigate the impact of penicillin allergy labels and compare the factors associated with HR-QoL impairment among patients in an international multicenter, multiethnic cohort. Methods HR-QoL was measured using the 6-item Drug Hypersensitivity Quality of Life Questionnaire (DrHy-Q) and compared among patients labeled with penicillin allergy, before their allergy evaluation, from 8 adult allergy/immunology clinics across Asia, Australia, and North America. Results We recruited 643 patients labeled with penicillin allergy (median age, 56 years [interquartile range, 39-67]; male:female ratio, 1:2.2), with 273 (42.5%), 186 (28.9%), and 184 (28.6%) from Asia, North America, and Australia, respectively. The median DrHy-Q score was 8.3 (interquartile range, 0.0-29.2). All patients underwent penicillin allergy evaluation, and 96% (617 of 643) were delabeled following negative provocation test results. Female patients (8.3 vs 4.2; P = .003), those with other concomitant antimicrobial allergy labels (20.8 vs 4.2; P = .004), and patients from Asia (33.3 vs 4.2 [North America] vs 0 [Australia]; P < .001) had significantly higher DrHy-Q scores, reflecting a reduced HR-QoL. Ethnicity as well as other allergy variables were not significant in the multivariate analysis. Conclusions Regional differences, ethnicity, and other risk factors influence HR-QoL impairment among patients labeled with penicillin allergy. Future studies are needed to understand the contributions of regional sociodemographic factors and identify interventions to improve HR-QoL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".