Bibliographic record
Abstract
PURPOSE OF REVIEW: Neurosyphilis (NS) and Lyme neuroborreliosis (LNB) are spirochetal diseases with distinct clinical manifestations. The diagnosis of NS remains challenging due to imperfect diagnostic criteria and testing modalities. With LNB, misconceptions about diagnosis and treatment lead to considerable morbidity and drug related adverse effects. RECENT FINDINGS: Although studies continue investigating alternate approaches and new diagnostic tests for NS, few data exist to change current approaches to diagnosis, management or follow up. In the diagnosis of LNB, the chemokine CXCL13 shows promising diagnostic accuracy. A systematic review discourages the use of cell-based assays when investigating Lyme disease. Clinical studies show no benefit from extended antibiotic treatment for patients with unspecific symptoms labelled as having Lyme disease. SUMMARY: The diagnosis of NS may be delayed due to a lack of specificity of findings, low suspicion for syphilis, and/or similarities in presentation to other diseases. A high index of suspicion for syphilis is required provide timely diagnosis and management of NS. Fortunately, penicillin remains the treatment of choice. Overdiagnosis and overtreatment in patients labelled as having Lyme disease can be avoided by an evidence-based approach towards diagnosis and treatment.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".