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Record W2005776181 · doi:10.1517/14656566.7.5.607

Sativex<sup>®</sup>: clinical efficacy and tolerability in the treatment of symptoms of multiple sclerosis and neuropathic pain

2006· review· en· W2005776181 on OpenAlexaboutno aff
Michael P. Barnes

Bibliographic record

VenueExpert Opinion on Pharmacotherapy · 2006
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNeuropathic painTolerabilityMultiple sclerosisCannabidiolDosingSpasticityPlaceboClinical trialChronic painGabapentinAnesthesiaPharmacologyPhysical therapyInternal medicineCannabisAdverse effectAlternative medicinePsychiatry

Abstract

fetched live from OpenAlex

Sativex is one of the first cannabis-based medicines to undergo conventional clinical development and to be approved as a prescription medicine. It is an oromucosal spray that allows flexible, individualised dosing. Patients self titrate their overall dose and pattern of dosing according to their response to and tolerance of the medicine. This usually results in the administration of approximately 8-12 sprays/day. Each spray delivers tetrahydrocannabinol 2.7 mg and cannabidiol 2.5 mg, giving an approximate average dose of tetrahydrocannabinol 22-32 mg/day and cannabidiol 20-30 mg/day. Development has concentrated on the treatment of symptoms of multiple sclerosis, notably spasticity and neuropathic pain, as well as the treatment of neuropathic pain of other aetiologies. Positive results in placebo-controlled trials of the use of Sativex as an add-on therapy in these indications demonstrate that Sativex is efficacious and well tolerated in the treatment of these symptoms. Sativex has been approved for use in neuropathic pain due to multiple sclerosis in Canada. If ongoing studies replicate the results already observed, further approvals for the treatment of spasticity in multiple sclerosis and for neuropathic pain are likely.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.149
GPT teacher head0.449
Teacher spread0.300 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations158
Published2006
Admission routes1
Has abstractyes

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