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Record W3200298861 · doi:10.1177/02692163211045314

Cannabinoids for the treatment of refractory neuropathic pruritus in amyotrophic lateral sclerosis: A case report

2021· article· en· W3200298861 on OpenAlexaff
Kelvin Lou, Shane Murphy, Clair Talbot

Bibliographic record

VenuePalliative Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNeuropathic painMedicineRiluzoleAmyotrophic lateral sclerosisRefractory (planetary science)NeuralgiaSide effect (computer science)AnesthesiaDermatologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Neuropathic symptoms have a wide variety of manifestations, ranging from pain to pruritus. Neuropathic pruritus is a type of chronic pruritus related to damaged small fibers. Cannabinoids have evidence to manage neuropathic symptoms. We present a case of refractory neuropathic pruritus that was successfully managed with the use of oral cannabinoids. CASE PRESENTATION: A 60-year-old male with amyotrophic lateral sclerosis with ongoing pruritus despite the use of standard neuropathic therapies. POSSIBLE COURSE OF ACTION: -methyl-D-aspartate receptor antagonists have evidence for neuropathic symptoms but can cause significant gastrointestinal side effects. Prescription cannabinoids such as nabiximol can be cost prohibitive to use in practice. Synthetic tetrahydrocannabinol products are dose limited by psychoactive side effects. FORMULATION OF A PLAN: A balanced oral cannabinoid from a licensed producer was preferred as it has evidence for neuropathic symptoms and is generally well tolerated. OUTCOME: The patient showed improvement to his pruritus score from 7/10 to 3/10. There was initial increased sedation but tolerance developed quickly. LESSONS LEARNED FROM CASE: Cannabinoids are possibly safe and effective in management of neuropathic pruritus. VIEW ON RESEARCH PROBLEMS: Additional research is needed to establish efficacy and safety.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.458
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.066
GPT teacher head0.351
Teacher spread0.285 · 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

Citations12
Published2021
Admission routes1
Has abstractyes

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