Billing fees for various common allergy tests vary widely across Canada
Bibliographic record
Abstract
BACKGROUND: The prevalence of food allergy in Canada is high and has increased over time. To date, there are no Canadian data on the healthcare costs of visits to allergists. METHODS: We sent an anonymous survey to allergist members of the Canadian Society of Allergy and Clinical Immunology (CSACI) between October and December 2019. Survey questions included demographic information and billing fees for various types of allergy visits and diagnostic testing. RESULTS: Of 200 allergists who are members of CSACI, 43 allergists responded (21.5% response rate). Billing fees varied widely. The greatest ranges were noted for oral immunotherapy (OIT; both initial consultation [mean $198.70; range $0 to $575] and follow up/build up visits [mean $125.74; range: $0 to $575]). There were significant provincial differences in billing fees, as well as significant billing fee differences between hospital versus community allergists (e.g. oral food challenge [OFC]: $256.38 vs. $134.94, p < 0.01). Billing fees were higher outside of Ontario, with the exception of specific Immunoglubulin E (sIgE) testing and OIT visits. CONCLUSIONS: Greater standardization of billing fees across provinces and between hospital versus community allergy could result in more consistency of billing fees for OFC and OIT across Canada. Further knowledge of exact costs will help inform practice and policy in the diagnosis and management of food allergy.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| 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) | 0.006 | 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".