Comparing Canadian Lyme disease risk area classification methodologies
Bibliographic record
Abstract
Lyme disease risk areas have increased across Canada in recent decades with the ongoing range expansion of Ixodes scapularis and Borrelia burgdorferi. Different methodologies are used by federal and provincial governments to determine local Lyme disease risk, which can make comparisons between regions challenging. In this study, seven Canadian Lyme disease risk classification methodologies were compared with each other to highlight the strengths and limitations of how each definition measured I. scapularis and B. burgdorferi risk. Each methodology was applied to active surveillance data from Ontario, and per cent agreement and kappa statistics were calculated. The methodologies varied in their measurements of the risk of exposure to I. scapularis and B. burgdorferi based on their use of active surveillance techniques, multiple types of collected surveillance data and laboratory confirmation of B. burgdorferi. Most initial Lyme disease risk site classifications were maintained over time. Kappa and per cent agreement statistics highlighted large differences between 8 of the 15 methodology pairings, indicating the presence of inconsistencies between most methodologies. Accurate, consistent surveillance and assessment of the spread of I. scapularis and its pathogens will aid with communicating Lyme disease risk to the public and preventing tick-borne pathogen transmission.
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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 | 0.025 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".