Gabapentin for the Symptomatic Treatment of Chronic Neuropathic Pain in Patients with Late-Stage Lyme Borreliosis: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Chronic neuropathic pain occurs in 10-15% of patients with neuroborreliosis and is difficult to treat. OBJECTIVE: We evaluated the effect of gabapentin monotherapy on residual pain in patients with neuroborreliosis after intravenous ceftriaxone treatment. METHODS: Ten patients with neuroborreliosis and a long-lasting history of neurologic symptoms were treated with gabapentin, starting with 300 mg/day. Doses were raised over a period of 4-12 weeks to the individually effective and tolerated maximum dose (500-1,200 mg). Treatment was maintained until pain disappeared and then gradually reduced in dose over weeks. If symptoms recurred, the doses were raised again. Therapy was maintained over an average of 1-2 years. RESULTS: Pain quality and pain quantity were evaluated using the McGill pain questionnaire and a visual analogue scale. There was an improvement of 'crawling' and 'burning' pain sensations, neck and radiating lumbar pain in 9/10 (90%) patients as well as a positive effect on mood, general feeling of health and quality of sleep in 5/10 (50%) patients. The average dose leading to a clear-cut pain reduction was 700 mg. CONCLUSIONS: In an open pilot study (10 patients), gabapentin monotherapy which has to our knowledge not been published as treatment of chronic neuropathic pain in patients with late Lyme borreliosis is efficacious in treating pain associated with neuroborreliosis and can thus improve quality of life in these patients.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".