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Record W3010997532 · doi:10.1080/09273948.2020.1726969

Cannabis and the Cornea

2020· review· en· W3010997532 on OpenAlex
Anne Xuan-Lan Nguyen, Albert Y. Wu

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

VenueOcular Immunology and Inflammation · 2020
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsMcGill University
FundersNational Eye Institute
KeywordsCannabisCorneaMedicineCannabinoidEffects of cannabisLegalizationOphthalmologyPharmacologyPsychiatryInternal medicineCannabidiolReceptor

Abstract

fetched live from OpenAlex

Purpose: While cannabis has the potential to reduce corneal pain, cannabinoids might induce side effects. This review article examines the effects of cannabinoids on the cornea. As more states and countries consider the legalization of adult cannabis use, health-care providers will need to identify ocular effects of cannabis consumption.Methods: Studies included in this review examined the connection between cannabis and the cornea, more specifically anti-nociceptive and anti-inflammatory actions of cannabinoids. NCBI Databases from 1781 up to December 2019 were consulted.Results: Five studies examined corneal dysfunctions caused by cannabis consumption (opacification, decreased endothelial cell density). Twelve studies observed a reduction in corneal pain and inflammation (less lymphocytes, decreased corneal neovascularization, increased cell proliferation and migration).Conclusion: More than half of the studies examined the therapeutic effects of cannabinoids on the cornea. As the field is still young, more studies should be conducted to develop safe cannabinoid treatments for corneal diseases.

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 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 score0.993
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.297
Teacher spread0.280 · 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