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Record W2108715039 · doi:10.14740/cr398w

Did Cannabis Precipitate an STEMI in a Young Woman?

2015· article· en· W2108715039 on OpenAlexvenueno aff
Waqas Jehangir, Michael Stanton, Rafay Khan, Puneet Sahgal, Abdalla Yousif

Bibliographic record

VenueCardiology Research · 2015
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
Fundersnot available
KeywordsCannabisDelta-9-tetrahydrocannabinolCannabinoidMedicineTetrahydrocannabinolCannabinoid receptorMyocardial infarctionEndocannabinoid systemEffects of cannabisReceptorInternal medicinePharmacologyCardiologyPsychiatryCannabidiol

Abstract

fetched live from OpenAlex

Cannabis is a substance that contains compounds that bind cannabinoid receptors, CB1 and CB2. Cannabis also contains substances that do not bind these receptors. Delta-9-tetrahydrocannabinol (THC) is the compound in cannabis responsible for its psychoactive effects and binding to cannabinoid receptors. Despite increasing popularity of the medical and recreational uses of cannabis, little attention has been paid to the adverse effects of the use of the substance. Evidence demonstrating an association between cannabis use and acute coronary syndromes has emerged with case reports and in vitro studies. This case report highlights an ST-segment myocardial infarction in a 27-year-old female with little cardiovascular risk factors, but a significant history of frequent cannabis use. Cardiol Res. 2015;6(3):283-285 doi: http://dx.doi.org/10.14740/cr398w

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.120
GPT teacher head0.425
Teacher spread0.304 · 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 designCase report
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

Citations8
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueCardiology Research→Same topicCannabis and Cannabinoid Research→French-language works237,207→