MétaCan
Menu
Back to cohort
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 OpenAlex
Jacob Cohen, Zelan Wei, Jonathan Phang, Robert B. Laprairie, Yanbo Zhang

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

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.168
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