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Nightmares and the Cannabinoids

2020· review· en· W3000185580 on OpenAlex
Mortimer Mamelak

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

VenueCurrent Neuropharmacology · 2020
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsBaycrest HospitalUniversity of Toronto
Fundersnot available
KeywordsΔ9-tetrahydrocannabinolNeuroscienceMedicineCannabinoidPsychologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

The cannabinoids, Δ9 tetrahydrocannabinol and its analogue, nabilone, have been found to reliably attenuate the intensity and frequency of post-traumatic nightmares. This essay examines how a traumatic event is captured in the mind, after just a single exposure, and repeatedly replicated during the nights that follow. The adaptive neurophysiological, endocrine and inflammatory changes that are triggered by the trauma and that alter personality and behavior are surveyed. These adaptive changes, once established, can be difficult to reverse. But cannabinoids, uniquely, have been shown to interfere with all of these post-traumatic somatic adaptations. While cannabinoids can suppress nightmares and other symptoms of post-traumatic stress disorder, they are not a cure. There may be no cure. The cannabinoids may best be employed, alone, but more likely in conjunction with other agents, in the immediate aftermath of a trauma to mitigate or even abort the metabolic changes which are set in motion by the trauma and which may permanently alter the reactivity of the nervous system. Steps in this direction have already been taken.

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)0.000
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 score0.748
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.061
GPT teacher head0.417
Teacher spread0.357 · 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