Regional diagnostic panels for aeroallergens in Canada
Bibliographic record
Abstract
Prevalence of allergenic plants varies by geographic region and climate, which affects the level of allergen exposure experienced by patients in different parts of the country. Because of these variations in exposure it is recommended that allergy practices use customized regional diagnostic panels based on the prevalence and significance of various aeroallergens. However, gathering this information can be difficult for new physicians. The purpose of these recommendations is to provide a foundation for new physicians to begin building a custom aeroallergen panel for all regions of Canada. Clinical, geographical and botanical references were evaluated and compiled to determine the prevalence and impact of various aeroallergens across Canada. These recommendations were discussed with regional allergy practices and other clinical authorities for consensus on recommendations. Aeroallergen recommendations were compiled into a prevalence map and table that was organized by the major geographic regions of Canada. Allergens were categorized as (1) high allergenicity & high prevalence, (2) high allergenicity & low prevalence, (3) low allergenicity & high prevalence, or (4) low allergenicity & low prevalence. A significant degree of allergen similarity across all regions was recognized although specific differences in species selection and general distribution patterns were identified for each region. Gathering clinical and botanical prevalence data for aeroallergens across different regions can be time consuming and difficult. These recommendations were compiled from many years of industry experience working with allergy specialists across the country and were verified by the literature. It is hoped that this knowledge can provide a foundation for new physicians trying to understand which aeroallergens to target for allergy diagnostic panels specific to their region.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".