Lyme disease in UK primary care: a knowledge, attitude, and practice survey
Bibliographic record
Abstract
BACKGROUND: Lyme disease (LD) cases in the UK most commonly present within the primary care setting. Despite an upward trend of incidence, little is known regarding GP experience with diagnosis and treatment. AIM: This study aims to describe baseline primary care clinician Knowledge, Attitude and Practice (KAP) in Scotland and England. DESIGN & SETTING: Online KAP survey on LD for UK-based practising GPs. METHOD: An online KAP questionnaire was developed for use in UK primary care. The survey was distributed through UK-based research networks, professional societies, and via social media. RESULTS: = 61). The Scotland-based responder group had more relevant consultations in the previous 3 years. Responders from Scotland demonstrated a greater awareness that erythema migrans (EM) is pathognomonic for LD and that serological testing of this patient group is not indicated. Less common cardiac and neurological symptoms were not as well associated with LD by both responder groups for the former and England-based responders for the latter. Prescribing according to the National institute for Health and Care Excellence (NICE) guidance was identified in 70% of Scotland and 42% of England-based GP responses. CONCLUSION: Targeted resources may improve clinician confidence on exposure risk, symptom recognition, testing limitations and treatment dose and duration. Scotland-based responders' better survey performance potentially reflects greater clinical exposure and public awareness of the disease, due to high endemicity within the nation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".