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Record W2346110970 · doi:10.1155/2005/763623

Toxic Effects of Cannabis and Cannabinoids: Animal Data

2005· review· en· W2346110970 on OpenAlexaff
Pierre Beaulieu

Bibliographic record

VenuePain Research and Management · 2005
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsHôtel-Dieu de Montréal
Fundersnot available
KeywordsCannabisTetrahydrocannabinolToxicitySynthetic cannabinoidsDelta-9-tetrahydrocannabinolDronabinolNeurotoxicityAcute toxicityPharmacologyCannabinoidMedicineEffects of cannabisChronic toxicityΔ9-tetrahydrocannabinolCannabidiolToxicologyBiologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

The present article reviews the main toxic effects of cannabis and cannabinoids in animals. Toxic effects can be separated into acute and chronic classifications. Acute toxicity studies show that it is virtually impossible to die from acute administration of marijuana or tetrahydrocannabinol, the main psychoactive component of cannabis. Chronic toxicity involves lesions of airway and lung tissues, as well as problems of neurotoxicity, tolerance and dependence, and dysregulations in the immune and hormonal systems. Animal toxicity data, however, are difficult to extrapolate to humans.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.696
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.003
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 head0.106
GPT teacher head0.438
Teacher spread0.332 · 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

Citations31
Published2005
Admission routes1
Has abstractyes

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