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Record W2564584662 · doi:10.1080/00085030.2017.1258212

An assessment of oral fluid drug screening devices

2016· article· en· W2564584662 on OpenAlexaffvenueabout
D J Beirness, D'Arcy Randall Smith

Bibliographic record

VenueCanadian Society of Forensic Science Journal · 2016
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicForensic Toxicology and Drug Analysis
Canadian institutionsRoyal Canadian Mounted PoliceCanadian Centre on Substance Use and Addiction
Fundersnot available
KeywordsDrugMedicineCannabisDrug detectionPharmacologyAmphetamineInternal medicinePsychiatryChromatographyChemistry

Abstract

fetched live from OpenAlex

This project was to examine point-of-contact (POC) oral fluid drug screening devices to determine the suitability of such devices for potential use in the enforcement of drug-impaired driving in Canada. Oral fluid samples were collected from a group of individuals who admitted to having recently ingested drugs as well as a number of individuals who had not been using drugs. These samples were tested on one of three oral fluid screening devices to determine the presence of cannabis, cocaine, amphetamine, methamphetamine, opioids, and benzodiazepines. Each participant also provided a second oral fluid sample that was sent to a reference laboratory for independent analysis. Comparison of the results from the oral fluid screening device and those from the laboratory analysis provided estimates of sensitivity and specificity for each of the six drugs/drug categories. Sensitivity exceeded 0.80 for cannabis, cocaine, methamphetamine, and opioids. False positive rates for these drugs/drug categories were all between 3% and 7%. Specificity exceeded 0.90 for all drugs/drug categories. These findings indicate that oral fluid screening could prove to be a valuable tool in the detection of driver drug use in 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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, 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 score0.380
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.004
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.066
GPT teacher head0.430
Teacher spread0.364 · 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

Citations19
Published2016
Admission routes3
Has abstractyes

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