Chronic or Late Lyme Neuroborreliosis: Present and Future
Bibliographic record
Abstract
This special issue gives a framework of an international effort, to critically and constructively overview the clinical and pathological aspects of Lyme neuroborreliosis and show directions for future practice and research.The issue in the diagnosis and treatment of Lyme neuroorreliosis is assessed followed by a comprehensive analysis of the involvement of connective tissue and associated clinical manifestations.A critical review shows that both the meningovascular and meningoencephalitic forms, which define chronic or late neurosyphilis also occur in Lyme neuroborreliosis.Clinical and pathological confir-mation of these tertiary forms and detection of Borrelia burgdorferi in association with tertiary brain lesions were reported by many authors.These observations indicate that similarly to Treponema pallidum, Borrelia burgdorferi infection is directly involved in the late or chronic manifestations of Lyme neuroborreliosis.Chronic or late Lyme neuroborreliosis both refer to tertiary neuroborreliosis, therefore, the use of these terms as different entities is not justified and may lead to confusion.A critical assessment of clinical trials will guide the design of future clinical studies and a detailed analysis of various factors influencing PCR detection of Borrelia specific DNA would be precious to improve the sensitivity of this potentially important diagnostic tool.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".