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Record W2530976263 · doi:10.1097/jcp.0000000000000581

Targeting the Endocannabinoid System in Psychiatric Illness

2016· review· en· W2530976263 on OpenAlexaff
Martin A. Katzman, Melissa Furtado, Leena Anand

Bibliographic record

VenueJournal of Clinical Psychopharmacology · 2016
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsNOSM UniversityLakehead UniversityAdlerSTART Clinic
Fundersnot available
KeywordsTolerabilityPsychiatryEndocannabinoid systemCannabidiolCannabinoidMedicineAnxietyCannabisSchizophrenia (object-oriented programming)PopulationAdverse effectPharmacologyInternal medicine

Abstract

fetched live from OpenAlex

Prevalence of psychiatric disorders continues to rise globally, yet remission rates and patient outcome remain less than ideal. As a result, novel treatment approaches for these disorders are necessary to decrease societal economic burden, as well as increase individual functioning. The recent discovery of the endocannabinoid system has provided an outlet for further research into its role in psychiatric disorders, because efficacy of targeted treatments have been demonstrated in medical illnesses, including cancers, neuropathic pain, and multiple sclerosis. The present review will investigate the role of the endocannabinoid system in psychiatric disorders, specifically schizophrenia, depressive, anxiety, and posttraumatic stress disorders, as well as attention-deficit hyperactivity disorder. Controversy remains in prescribing medicinal cannabinoid treatments due to the fear of adverse effects. However, one must consider all potential limitations when determining the safety and tolerability of cannabinoid products, specifically cannabinoid content (ie, Δ-tetrahydrocannabinol vs cannabidiol) as well as study design. The potential efficacy of cannabinoid treatments in the psychiatric population is an emerging topic of interest that provides potential value going forward in medicine.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.702
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0010.000

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 head0.065
GPT teacher head0.492
Teacher spread0.427 · 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

Citations29
Published2016
Admission routes1
Has abstractyes

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