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Record W4294250055 · doi:10.1089/can.2022.0158

Evaluation of Sex Differences in the Potential of Δ <sup>9</sup> -Tetrahydrocannabinol, Cannabidiol, Cannabidiolic Acid, and Oleoyl Alanine to Reduce Nausea-Induced Conditioned Gaping Reactions in Sprague-Dawley Rats

2022· article· en· W4294250055 on OpenAlexaff
Erin M. Rock, Cheryl L. Limebeer, Reem Smoum, Raphael Mechoulam, Linda A. Parker

Bibliographic record

VenueCannabis and Cannabinoid Research · 2022
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsNauseaCannabidiolDronabinolPharmacologyCannabinoidLithium (medication)ConditioningTaste aversionChemistryInternal medicineMedicineCannabisTasteBiochemistryPsychiatry

Abstract

fetched live from OpenAlex

Introduction: Cancer patients report nausea as a side effect of their chemotherapy treatment. Using the pre-clinical rodent model of acute nausea—lithium chloride (LiCl)-induced conditioned gaping—our group has demonstrated that exogenous cannabinoids may have antinausea potential. Materials and Methods: With the goal of evaluating the role of sex as a factor in pre-clinical research, we first compared the conditioned gaping reactions produced by varying doses of LiCl in male and female rats using the taste reactivity test (Experiment 1). Results: LiCl produced dose-dependent conditioned gaping similarly in male and female rats with the highest dose (127.2 mg/kg) producing robust conditioned gaping, with this dose used in subsequent experiments. Next, we examined the antinausea potential of THC (Experiment 2), CBD (Experiment 3), cannabidiolic acid (CBDA; Experiment 4) and oleoyl alanine (OlAla; Experiment 5) in both male and female rats. THC, CBD, CBDA, and OlAla dose dependently reduced conditioned gaping in both male and female rats in a similar manner. Conclusions: These results suggest that cannabinoids may be equally effective in treating nausea in both males and females.

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)0.015
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.749
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.071
GPT teacher head0.362
Teacher spread0.291 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations5
Published2022
Admission routes1
Has abstractyes

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