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Record W2091746101 · doi:10.1159/000086441

Gabapentin for the Symptomatic Treatment of Chronic Neuropathic Pain in Patients with Late-Stage Lyme Borreliosis: A Pilot Study

2005· article· en· W2091746101 on OpenAlexaboutno aff
S. Weissenbacher, J. Ring, Heidelore Hofmann

Bibliographic record

VenueDermatology · 2005
Typearticle
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGabapentinNeuroborreliosisPregabalinNeuropathic painAnesthesiaNeuralgiaSurgery

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.596

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.230
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations30
Published2005
Admission routes1
Has abstractyes

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