MétaCan
Menu
← Back to cohort
Record W2909780369 · doi:10.3390/ijms20020388

Modulation of the Endocannabinoid System Following Central Nervous System Injury

2019· review· en· W2909780369 on OpenAlexaff
Juan Zhou, Haneen Noori, Ian Burkovskiy, J. Daniel Lafreniere, Christine Lehmann

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2019
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsEndocannabinoid systemCentral nervous system2-ArachidonoylglycerolNeuroscienceNervous systemModulation (music)MedicineBiologyCannabinoid receptorInternal medicineReceptorPhysics

Abstract

fetched live from OpenAlex

Central nervous system (CNS) injury, such as stroke or trauma, is known to increase susceptibility to various infections that adversely affect patient outcomes (CNS injury-induced immunodepression-CIDS). The endocannabinoid system (ECS) has been shown to have immunoregulatory properties. Therefore, the ECS might represent a druggable target to overcome CIDS. Evidence suggests that cannabinoid type 2 receptor (CB₂R) activation can be protective during the early pro-inflammatory phase after CNS injury, as it limits neuro-inflammation and, therefore, attenuates CIDS severity. In the later phase post CNS injury, CB₂R inhibition is suggested as a promising pharmacologic strategy to restore immune function in order to prevent infection.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.031
GPT teacher head0.357
Teacher spread0.326 · 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 designNot applicable
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

Citations22
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Molecular Sciences→Same topicCannabis and Cannabinoid Research→French-language works237,207→