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Record W2927512341 · doi:10.1093/jat/bkz018

Challenges Related to Three Cases of Fatal Intoxication to Multiple Novel Synthetic Opioids

2019· article· en· W2927512341 on OpenAlexaffabout
Béatrice Garneau, Brigitte Desharnais, Audrey Beauchamp-Doré, Catherine Lavallée, Pascal Mireault, André Lajeunesse

Bibliographic record

VenueJournal of Analytical Toxicology · 2019
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsConcordia UniversityUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsForensic toxicologySynthetic cannabinoidsMedicineBiological fluidsUrineMedical prescriptionPoison controlDesigner drugEmergency medicineToxicologyPharmacologyIntensive care medicineInternal medicineChemistryDrugBiologyChromatography

Abstract

fetched live from OpenAlex

In the last two decades, a large increase in opioid overdose death rates has been recorded in North America. This phenomenon, related to the misuse of prescription opioids, has been dubbed an "opioids crisis". Recent years have seen the entrance of novel synthetic opioids (NSO) on the market, compounding the fatal intoxications issue. This brings several challenges for forensic toxicology laboratories: an increased number of cases, a large number of novel structurally similar compounds to include in screening analytical methods, the low concentration of drugs in biological fluids, and the challenging interpretation in the absence of sufficient literature. Three cases of fatal intoxication highlighting those challenges are presented, complete with post-mortem concentrations in cardiac blood, femoral blood and urine. Toxicological screening and quantitative analyses were performed on the biological specimens. In the first and second cases, furanylfentanyl, U-47700 and 4-anilino-N-phenethylpiperidine (4-ANPP) were detected at similar concentrations in cardiac blood. In the third case, a total of seventeen different NSO were detected. All intoxications showed a combination of NSO and other drugs. These three cases appear to be the harbinger of an increased NSO prevalence in the province of Québec, Canada.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
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 head0.042
GPT teacher head0.330
Teacher spread0.288 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations22
Published2019
Admission routes2
Has abstractyes

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