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Record W2342372197 · doi:10.1089/can.2016.0006

Cannabinoid Regulation of Acute and Anticipatory Nausea

2016· review· en· W2342372197 on OpenAlexafffund
Erin M. Rock, Martin A. Sticht, Cheryl L. Limebeer, Linda A. Parker

Bibliographic record

VenueCannabis and Cannabinoid Research · 2016
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsHotchkiss Brain InstituteUniversity of CalgaryUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsNauseaAprepitantOndansetronEndocannabinoid systemMedicineChemotherapyVomitingClinical trialAnesthesiaAntiemeticInternal medicine

Abstract

fetched live from OpenAlex

Chemotherapy-induced nausea is one of the most distressing symptoms reported by patients undergoing treatment, and even with the introduction of newer antiemetics such as ondansetron and aprepitant, nausea remains problematic in the clinic. Indeed, when acute nausea is not properly managed, the cues of the clinic can become associated with this distressing symptom resulting in anticipatory nausea for which no effective treatments are available. Clinical trials exploring the potential of exogenous or endogenous cannabinoids to reduce chemotherapy-induced nausea are sparse; therefore, we must rely on the data from pre-clinical rat models of nausea. In this review, we explore the human and pre-clinical animal literature examining the potential for exogenous and endogenous cannabinoid treatments to regulate chemotherapy-induced nausea. The pre-clinical evidence points to a compelling need to evaluate the antinausea potential of cannabidiol, cannabidiolic acid, and treatments that boost the functioning of the endocannabinoid system in human clinical trials.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.732
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0030.002
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.074
GPT teacher head0.417
Teacher spread0.343 · 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

Citations28
Published2016
Admission routes2
Has abstractyes

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