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Record W4386624716 · doi:10.1097/aln.0000000000004673

Use of Psychedelics for Pain: A Scoping Review

2023· review· en· W4386624716 on OpenAlexaff
Akash Goel, Yeshith Rai, Shayan Sivadas, Calvin Diep, Hance Clarke, Harsha Shanthanna, Karim S. Ladha

Bibliographic record

VenueAnesthesiology · 2023
Typereview
Languageen
FieldPsychology
TopicPsychedelics and Drug Studies
Canadian institutionsQueen's UniversityMcMaster UniversityUniversity of TorontoToronto General HospitalSt. Michael's Hospital
Fundersnot available
KeywordsPsilocybinMedicineChronic painHallucinogenAddictionOpioidAdverse effectCancer painLysergic acid diethylamideClinical trialDissociativePsychiatryIntensive care medicinePharmacologyCancerSerotoninInternal medicine

Abstract

fetched live from OpenAlex

Chronic pain is a public health concern that affects approximately 1.5 billion people globally. Conventional therapeutic agents including opioid and non-opioid analgesics have been associated with adverse side effects, issues with addiction, and ineffective analgesia. Novel agents repurposed to treat pain via different mechanisms are needed to fill the therapeutic gap in chronic pain management. Psychedelics such as lysergic acid diethylamide and psilocybin (the active ingredient in psychedelic mushrooms) are thought to alter pain perception through direct serotonin receptor agonism, anti-inflammatory effects, and synaptic remodeling. This scoping review was conducted to identify human studies in which psychedelic agents were used for the treatment of pain. Twenty-one articles that assessed the effects of psychedelics in treating various pain states were included. The present scarcity of clinical trials and small sample sizes limit their application for clinical use. Overall, psychedelics appear to show promise for analgesia in patients with certain headache disorders and cancer pain diagnoses. Future studies must aim to examine the combined effects of psychotherapy and psychedelics on chronic pain.

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)0.001
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 score0.698
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.520
GPT teacher head0.517
Teacher spread0.002 · 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
Published2023
Admission routes1
Has abstractyes

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