MétaCan
Menu
Back to cohort
Record W2774909823 · doi:10.1111/1556-4029.13704

Teens and Spice: A Review of Adolescent Fatalities Associated with Synthetic Cannabinoid Use

2017· review· en· W2774909823 on OpenAlexaff
Anthea B. Mahesan Paul, Lary Simms, Saeideh Amini, Abraham Ebenezer Paul

Bibliographic record

VenueJournal of Forensic Sciences · 2017
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicForensic Toxicology and Drug Analysis
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsSynthetic cannabinoidsForensic toxicologyAutopsyMedicinePoison controlDesigner drugCause of deathCannabisInjury preventionCannabinoidStreet drugsMedical jurisprudenceToxicologyEmergency medicineDrugPharmacologyInternal medicinePsychiatryChemistryChromatographyPathologyBiology

Abstract

fetched live from OpenAlex

Synthetic cannabinoids (SCs) are commonly abused by adolescents with reported past year (2013) use in high school students between 3 and 10%. Standard adolescent postmortem toxicology does not include routine SC analysis, and thus, the true burden of fatalities related to SCs is unknown. A retrospective case review of two cases included scene investigation, interviews, autopsy, and toxicology. SCs were confirmed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Review of the eight adolescent SC-associated fatalities in the literature revealed five of eight cases had no other discernible cause of death on autopsy. Compounds detected included PB-22 (1.1 ng/mL), JWH-210 (12 ng/mL), XLR-11 (1.3 ng/mL), JWH-122, AB-CHMINACA (8.2 ng/mL), UR-144 (12.3 ng/mL), and JWH-022 (3 ng/mL). With synthetic drug use on the rise, forensic experts should have a high index of suspicion for the possibility of SC intoxication in adolescent fatalities with no other discernible cause of death.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.286
GPT teacher head0.489
Teacher spread0.203 · 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 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

Citations52
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Forensic SciencesSame topicForensic Toxicology and Drug AnalysisFrench-language works237,207