Characterizing drug allergy management among allergists in Canada: a national survey study
Bibliographic record
Abstract
Abstract Background Unverified drug allergy labels are common and associated with significant patient harm, yet infrastructure and testing practices vary across clinical settings in Canada. Objective To characterize variability in drug allergy management among allergists in Canada and identify setting-specific barriers to drug allergy testing and desensitization. Methods We developed a peer-reviewed 40-item survey, distributed via the Canadian Society of Allergy and Clinical Immunology, to assess practice patterns, testing modalities, and perceived barriers among allergists. Descriptive statistics and Fisher’s exact test were used to evaluate responses by practice setting. Results Sixty-six allergists responded (30% estimated response rate), with 48.4% solely practicing in community clinics and 21.9% solely in hospital-based clinics. While 87.9% performed some form of drug allergy testing, hospital-based allergists were significantly more likely to perform intradermal (81.1% vs. 48.7%, p = 0.004) and patch testing (38.2% vs. 8.8%, p = 0.009), as well as non-oral drug challenges (63.6% vs. 20.0%, p = 0.0005). Common barriers included a lack of nursing support and inadequate reimbursement. Conclusion Drug allergy management practices vary substantially across Canada, with drug allergy testing being more frequently performed by allergists practicing in hospital-based clinics than by those in community-based clinics. Findings support the need for equitable access to testing infrastructure and system-level investments in improving drug allergy testing services.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".