Review of methods to prevent and reduce the risk of Lyme disease
Bibliographic record
Abstract
BACKGROUND: Cases of Lyme disease and areas with self-sustaining populations of vector ticks are increasing in Canada. This trend is expected to continue. Preventing Lyme disease will therefore become relevant to an increasing number of Canadians. OBJECTIVE: To summarize methods for reducing the risk of tick bites and preventing transmission once a tick is feeding. METHODS: (BB), if bitten by a vector tick. RESULTS: Current approaches to reducing the risk of tick bites or preventing infection with BB once bitten are largely reliant on the individual. They include use of topical repellents, use of protective clothing, avoidance of risk areas and removing ticks soon (ideally within a day) after they attach. These methods are efficacious, but constrained by user adherence. Other approaches such as landscape modification or the use of acaricides to control ticks, have shown promise in other countries, but have not been widely adopted in Canada. CONCLUSION: Lyme disease will continue to present a threat in Canada. In additional to the existing interventions for prevention of tick bites and Lyme disease, there is a need for new tools to help reduce the risk of Lyme disease to Canadians.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".