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Record W3207202320 · doi:10.3390/molecules26206257

Safety of Medical Cannabis in Neuropathic Chronic Pain Management

2021· review· en· W3207202320 on OpenAlexaboutno aff
Alessandra Bennici, Carmen Mannucci, Fabrizio Calapai, Luigi Cardia, Ilaria Ammendolia, Sebastiano Gangemi, Gioacchino Calapai, Daniel Griscti Soler

Bibliographic record

VenueMolecules · 2021
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
Fundersnot available
KeywordsCannabidiolNeuropathic painCannabisMedicineCannabis sativaChronic painAdverse effectPharmacovigilanceTetrahydrocannabinolIntensive care medicineNeuralgiaPharmacologyPsychiatryCannabinoidInternal medicine

Abstract

fetched live from OpenAlex

are widely appreciated for their analgesic properties and are employed for the treatment of chronic neuropathic pain. Only nabiximols, a product composed of two extracts containing similar percentages of the two cannabinoids cannabidiol and delta-9-tetrahydrocannabinol, is approved by regulatory authorities for neuropathic pain and spasticity due to multiple sclerosis in many European countries and Canada. It is also included in pharmacovigilance systems monitoring the occurrence of adverse drug reactions. However, it is not the same for the great variety of other cannabis preparations widely used for medical purposes. This creates a situation characterized by insufficient knowledge of the safety of cannabis preparations and the impossibility of establishing a correct risk-benefit profile for their medical use in the treatment of chronic neuropathic pain. With the aim to explore this issue more deeply, we collected data on adverse reactions from published clinical studies reporting the use of cannabis for neuropathic relief.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.024
GPT teacher head0.346
Teacher spread0.321 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations51
Published2021
Admission routes1
Has abstractyes

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