MétaCan
Menu
Back to cohort
Record W3045955198 · doi:10.1159/000508840

Cannabis and Inflammatory Mediators

2020· article· en· W3045955198 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEuropean Addiction Research · 2020
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsCannabisMedicineMultiple sclerosisImmune systemCannabinoidInflammationTetrahydrocannabinolEndocannabinoid systemImmunologyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION: Although the recreational cannabis use is expressive worldwide, the literature about medical potential of cannabis extracts, including its anti-inflammatory properties, remains inconclusive. METHODS: We screened all articles, published on the PubMed database, on inflammatory mediators and any information about cannabis use from 1980 to March 2019. RESULTS: Six studies were included, and the main findings were as follows: (i) among healthy volunteers and cannabis users, cannabinoids seemed to decrease the inflammatory response, thus decreasing the immune response, which led to a higher risk of infections; (ii) among patients with multiple sclerosis, cannabinoids seemed to have little impact on the inflammatory markers' levels. DISCUSSION: Although cannabis use can produce immune inflammatory suppression in healthy people, this effect is not robust enough to change inflammatory mediators' levels in situations of highly dysfunctional inflammatory activation. Nevertheless, the impact of cannabinoids in clinical outcomes of these conditions remains to be determined.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.251
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.043
GPT teacher head0.327
Teacher spread0.284 · 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