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Record W2997205944 · doi:10.1097/wnp.0000000000000612

Cannabinoids as an Emerging Therapy for Posttraumatic Stress Disorder and Substance Use Disorders

2019· review· en· W2997205944 on OpenAlexaff
Jacob Cohen, Zelan Wei, Jonathan Phang, Robert B. Laprairie, Yanbo Zhang

Bibliographic record

VenueJournal of Clinical Neurophysiology · 2019
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsDalhousie UniversityUniversity of Saskatchewan
Fundersnot available
KeywordsCannabisPsychiatryPosttraumatic stressSubstance useMedicineSubstance abuseClinical psychologyPsychology

Abstract

fetched live from OpenAlex

Posttraumatic Stress Disorder (PTSD) is a leading psychiatric disorder that mainly affects military and veteran populations but can occur in anyone affected by trauma. PTSD treatment remains difficult for physicians because most patients with PTSD do not respond to current pharmacological treatment. Psychotherapy is effective, but time consuming and expensive. Substance use disorder is often concurrent with PTSD, which leads to a significant challenge for PTSD treatment. Cannabis has recently received widespread attention for the potential to help many patient populations. Cannabis has been reported as a coping tool for patients with PTSD and preliminary legalization data indicate Cannabis use may reduce the use of more harmful drugs, such as opioids. Rigorous clinical studies of Cannabis could establish whether Cannabis-based medicines can be integrated into treatment regimens for both PTSD and substance use disorder patients.

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)0.001
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.168
GPT teacher head0.486
Teacher spread0.318 · 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

Citations15
Published2019
Admission routes1
Has abstractyes

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