Practices of Lyme disease diagnosis and treatment by general practitioners in Quebec, 2008–2015
Bibliographic record
Abstract
BACKGROUND: Lyme disease (LD), a multisystem infection caused by the spirochete Borrelia burgdorferi sensu stricto (B. burgdorferi), is the most reported vector-borne disease in North America, and by 2020, 80% of the population in central and eastern Canada could live in LD risk areas. Among the key factors for minimising the impact of LD are the accurate diagnosis and appropriate management of patients bitten by ticks. In this study, the practices of Quebec general practitioners (GPs) on LD diagnosis and management of patients bitten by infected ticks are described. METHODS: Eight years (2008 to 2015) of retrospective demographic and clinical data on patients bitten by infected Ixodes scapularis (I. scapularis) ticks and on the management of suspected and confirmed LD cases by Quebec GPs were analysed. RESULTS: Among 50 patients, all the antimicrobial treatments of LD clinical cases were appropriate according to current guidelines. However, more than half (62.8%) of erythema migrans (EM) were possibly misdiagnosed, 55.6%, (n = 27) of requested serologic tests were possibly unnecessary and the majority (96.5%, n = 57) of prophylactic antimicrobial treatments were not justified according to current guidelines. CONCLUSIONS: These observations underline the importance for public health to enhance the knowledge of GPs where LD is emerging, to minimise the impact of the disease on patients and the financial burden on the health system.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".