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Record W4282828353 · doi:10.3390/metabo12060541

The Endocannabinoid System: A Potential Therapeutic Target for Coagulopathies

2022· review· en· W4282828353 on OpenAlexaff
Wujood Khayat, Christine Lehmann

Bibliographic record

VenueMetabolites · 2022
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsEndocannabinoid systemMedicineCoagulationCoagulation systemCoagulopathyIntensive care medicineIschemiaCannabinoidPharmacologyNeuroscienceInternal medicineBiology

Abstract

fetched live from OpenAlex

Abnormal blood coagulation or coagulopathy is a common manifestation of many pathological conditions. It occurs when there is an imbalance between the activities of the coagulation system and the fibrinolytic system, leading to excessive or impaired intravascular blood clot formation, which can disturb blood flow causing ischemia or hemorrhage in the affected tissues. A growing body of evidence has demonstrated blood coagulation abnormalities in association with cannabinoid use, suggesting the involvement of the endogenous cannabinoid system (ECS) in modulating blood coagulation. However, the evidence in the literature has been controversial on whether cannabinoids promote or inhibit blood coagulation. The ECS has been extensively studied in recent years for its potential as a therapeutic target for many diseases. This review provides a brief introduction to the ECS and discusses the reported anticoagulatory and procoagulatory effects of various cannabinoids, highlighting some possible mechanisms that might underlie the observed effects. Understanding the coagulatory effects of cannabinoids and the interaction between the coagulation system and the ECS is vital for developing novel therapeutics for coagulopathies.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.960
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.047
GPT teacher head0.348
Teacher spread0.301 · 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 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

Citations15
Published2022
Admission routes1
Has abstractyes

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