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Record W2058383906 · doi:10.1002/acn3.163

Cannabinoid agonist rescues learning and memory after a traumatic brain injury

2015· article· en· W2058383906 on OpenAlexafffund
Marium Arain, Maida Khan, Laura A. Craig, Stan T. Nakanishi

Bibliographic record

VenueAnnals of Clinical and Translational Neurology · 2015
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersNational Institutes of HealthUniversity of Calgary
KeywordsAgonistTraumatic brain injuryMedicineCannabinoidCannabinoid receptorNeuroscienceAnesthesiaReceptorInternal medicinePsychologyPsychiatry

Abstract

fetched live from OpenAlex

Traumatic brain injury can cause persistent challenges including problems with learning and memory. Previous studies suggest that the activation of the cannabinoid 1 receptor after a traumatic brain injury could be beneficial. We tested the hypothesis that posttraumatic brain injury administration of a cannabinoid 1 receptor agonist can rescue deficits in learning and memory. Young adult male rats were subjected to a moderately severe controlled cortical impact brain injury, with a subset given postinjury i.p. injections of a cannabinoid receptor agonist. Utilizing novel object recognition and the morris water task, we found that the brain-injured animals treated with the agonist showed a marked recovery.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.128
GPT teacher head0.441
Teacher spread0.312 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations15
Published2015
Admission routes2
Has abstractyes

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