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Record W4399512713 · doi:10.1093/annweh/wxae048

Validation of a sampling method and liquid chromatography mass spectrometry analysis method for measurement of fentanyl and five other illicit drugs

2024· article· en· W4399512713 on OpenAlexaff
Matthew Jeronimo, Molly Mastel, Jasleen Gill, Hugh Davies

Bibliographic record

VenueAnnals of Work Exposures and Health · 2024
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicForensic Toxicology and Drug Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHarm reductionFentanylChromatographyEnvironmental healthPharmacologyMedicineEmergency medicineChemistryPublic healthNursing

Abstract

fetched live from OpenAlex

With the increased provision of services by health authorities and community organizations allowing supervised inhalation of illicit substances comes concerns about the potential for secondhand exposure to the substances being used, whether in the adjacent community or to workers at the sites. In order to address community concerns surrounding secondhand illicit substance exposure and better protect harm reduction workers, a validated sampling and LC-MS/MS analysis method was developed for 6 illicit drugs: fentanyl, heroin, methamphetamine, cocaine, etizolam, and bromazolam. It was found that the filter used needed to be silanized to be made more inert and avoid loss of analyte due to degradation. Using the silanized filters, recoveries were good (>90%) and the collected samples were found to be stable at room temperature for 2 wk. The sampling volume validated was up to 960 L. The sensitivity and range of the method make it appropriate for short-term (15 min), full shift (8 h), or environmental sampling.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.614
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.185
GPT teacher head0.497
Teacher spread0.312 · 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 designBench or experimental
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

Citations4
Published2024
Admission routes1
Has abstractyes

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