Geographical discrepancy in oral food challenge utilization based on Canadian billing data
Bibliographic record
Abstract
BACKGROUND: Oral food challenges (OFC) confer the highest sensitivity and specificity in diagnosis; however, uptake has been variable across clinical settings. Numerous barriers were identified in literature from inadequate training to resource access. OFC utilization patterns using billing data have not been previously studied. OBJECTIVE: The objective of this study is to explore the geographic differences in utilization of OFCs across Ontario and Québec using anonymized billing data from 2013 to 2017. METHODS: Anonymized OFC billing data were obtained between 2013 and 2017 from Ontario Health Insurance Plan (OHIP) and Régie de l'Assurance Maladie du Québec (RAMQ). The number of OFCs was extracted by location, billings, and physician demographics for clinic and hospital-based challenges. RESULTS: Over the period studied, the number of OFCs increased by 92% and 85% in Ontario clinics and Québec hospitals, respectively. For Ontario hospitals, the number of OFCs increased by 194%. While Québec performed exclusively hospital-based OFCs, after controlling for the population, the number of OFCs per 100,000 residents annually were similar to Ontario at 50 and 49 OFCs, respectively. The number of OFCs varied across the regions studied with an annual rate reaching up to 156 OFCs per 100,000 residents in urban regions and as low as 0.1 in regions furthest from city centers. CONCLUSION: OFC utilization has steadily increased over the last decade. There has been marked geographical discrepancies in OFC utilization which could be driven by the location of allergists and heterogeneity in their practices. More research is needed to identify barriers and propose solutions to them.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".