Bibliographic record
Abstract
BACKGROUND: Lyme disease is on the rise in Canada. It is a notifiable disease, and when infection is disseminated, serological testing provides supplemental evidence to confirm a case. OBJECTIVE: To describe the current diagnostic tests for Lyme disease, review the recommended approach to laboratory testing for Lyme disease and identify future research priorities for Lyme disease laboratory diagnostics in Canada. METHODS: A review of the literature was carried out. We then summarized parameters to consider before Lyme disease testing is conducted, described the current best practice to use a two-tiered diagnostic algorithm for the laboratory confirmation of disseminated Lyme disease, and analyzed the advantages and disadvantages of the supplemental tests for Lyme disease. RESULTS: Diagnostic testing is indicated in people who have symptoms of disseminated disease and a history of exposure to vector ticks. To maximize sensitivity and specificity, a two-tiered serological approach is recommended, consisting of an enzyme immunoassay (EIA) screening test followed by confirmation with Western blot (WB) testing. A number of other diagnostic tests are available; however, these are largely for research purposes. CONCLUSION: Two-tiered serology is currently the best approach available to assist doctors when they are making a diagnosis of disseminated Lyme disease. The Public Health Agency of Canada (the Agency) will seek to improve on this approach through standardization of the Lyme disease diagnostics used across laboratories in Canada, evaluation of test performance characteristics of current and new diagnostic platforms and development of a process to secure robust serum panels to assist in the development and evaluation of new diagnostic tests for Lyme disease.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".