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Record W4414895411 · doi:10.1080/17446651.2025.2572339

Should cannabis be used in the management of endometriosis?

2025· article· en· W4414895411 on OpenAlexaff
Mike Armour, Justin Sinclair, Callie Seaman, Amelia K. Mardon, Toobah Farooqi, Orit Holtzman, Mathew Leonardi

Bibliographic record

VenueExpert Review of Endocrinology & Metabolism · 2025
Typearticle
Languageen
FieldMedicine
TopicEndometriosis Research and Treatment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCannabisEndometriosisObservational studyMental healthEffects of cannabisRetrospective cohort study

Abstract

fetched live from OpenAlex

INTRODUCTION: Endometriosis is a chronic inflammatory condition that affects around 1 in 7 women of reproductive age. Current medical treatments tend to be sub-optimal to manage the range of symptoms, with low levels of patient satisfaction. Cross-sectional and retrospective data suggests that people with endometriosis are consuming cannabis to help manage their symptoms. AREAS COVERED: This review discusses the evidence for consumption of medicinal cannabis to help manage endometriosis symptoms, including potential mechanisms of action from both animal models and human studies, usage in the community, the current evidence from clinical trials and observational studies, and the safety and potential drug interactions. EXPERT OPINION: While there is a lack of high-quality clinical trial evidence, significant self-reported evidence from cross-sectional surveys and retrospective observational data suggests that those consuming medicinal cannabis report reductions in endometriosis symptoms such as pelvic pain, dysmenorrhea and gastrointestinal symptoms, and improve mental health and sleep. Given the low levels of satisfaction with current treatment options, consideration should be given to trialing medicinal cannabis as part of the interdisciplinary management of endometriosis in those who express interest and who do not demonstrate any significant contraindications.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.847
Threshold uncertainty score0.579

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.048
GPT teacher head0.405
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, 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

Citations2
Published2025
Admission routes1
Has abstractyes

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