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Record W196845801 · doi:10.1520/jfs15069j

Detection of Benzodiazepines in Different Tissues, Including Bone, Using a Quantitative ELISA Assay

2001· article· en· W196845801 on OpenAlexaff
LY Gorczynski, FJ Melbye

Bibliographic record

VenueJournal of Forensic Sciences · 2001
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicForensic Toxicology and Drug Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMidazolamDiazepamClearancePharmacokineticsPharmacologySedativeMedicineChemistryChromatographySedationUrology

Abstract

fetched live from OpenAlex

Benzodiazepines were analyzed in different tissue samples, including hone, by ELISA. The sensitivity of detection for different benzodiazepines was consistent with the manufacturer's reports of the cross reactivities of the antibodies used, with the greatest sensitivity for midazolam and the least for diazepam; in addition the pharmacokinetics was consistent with the known duration of action of the different benzodiazepines, with midazolam cleared rapidly, and diazepam slowly. Following intramuscular injection of 300 microg of midazolam at 16 h intervals for ten days, the drug was detectable in bone tissue samples obtained from skeletonized remains buried in soil at room temperature for three weeks.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.201
GPT teacher head0.469
Teacher spread0.268 · 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 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

Citations40
Published2001
Admission routes1
Has abstractyes

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