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Record W3201828807 · doi:10.1093/jat/bkab106

Etizolam Blood Concentrations in 191 Forensic Cases in Ontario, Canada (2019–2020)

2021· article· en· W3201828807 on OpenAlexaboutno aff
Christena Watts, T.L. Martin

Bibliographic record

VenueJournal of Analytical Toxicology · 2021
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicForensic Toxicology and Drug Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsForensic toxicologyForensic scienceMedicineSexual assaultDrugBenzodiazepinePsychiatryPharmacologyPoison controlEmergency medicineInternal medicineInjury preventionChemistryVeterinary medicineChromatography

Abstract

fetched live from OpenAlex

Although not used clinically in North America, etizolam has been identified in forensic samples as an illicit 'designer' benzodiazepine. As a central nervous system depressant, analysis for etizolam has probative value in both death investigations and forensic cases where incapacitation or human psychomotor performance is relevant. This report examines toxicological findings and demographic data in a series of authentic forensic cases analyzed between November 2019 and December 2020 in which etizolam was quantified by liquid chromatography-tandem mass spectrometry analysis. Blood concentrations were determined in 191 individuals aged 1-75 years. In living individuals (i.e., impaired driving and sexual assaults), etizolam concentrations ranged from <5 to 767 ng/mL which overlapped with the range of <5 to 260 ng/mL reported in death investigations. In all but one case, other drugs were detected in combination with etizolam. Fentanyl was the most common co-occurring drug and was present in 164 cases (86%). Additional case details are provided for cases of forensic interest: two deaths involving children <3 years of age, two deaths involving body packing and an individual arrested for drug-impaired driving with, to our knowledge, the highest reported etizolam concentration to date.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0080.000

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 head0.052
GPT teacher head0.366
Teacher spread0.315 · 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 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

Citations18
Published2021
Admission routes1
Has abstractyes

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