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Record W2163884092 · doi:10.1093/jat/28.6.537

Metaxalone (Skelaxin(R))-Related Death

2004· article· en· W2163884092 on OpenAlexaff
Justin L. Poklis, Jeri D. Ropero‐Miller, Diana Garside, Ruth E. Winecker

Bibliographic record

VenueJournal of Analytical Toxicology · 2004
Typearticle
Languageen
FieldMedicine
TopicPoisoning and overdose treatments
Canadian institutionsOffice of the Chief Medical Examiner
Fundersnot available
KeywordsForensic toxicologyNortriptylineChromatographyUrineHydrocodoneChemistryGas chromatography–mass spectrometryAcetaminophenMedicinePharmacologyMass spectrometryAmitriptylineInternal medicineOxycodone

Abstract

fetched live from OpenAlex

The case history and toxicological findings of a fatal multi-drug overdose involving metaxalone (Skelaxin) are presented. Gas-liquid chromatography with flame-ionization detection and gas chromatography-mass spectrometry were used to determine the following drug concentrations (mg/L) in aortic blood: 19 mg/L metaxalone; 190 mg/L acetaminophen; 0.28 mg/L hydrocodone; and < 0.1 mg/L diazepam, nordiazepam, amitriptyline, and nortriptyline. The following concentrations of metaxalone were reported in alternate specimens: 17 mg/L in femoral blood; 44 mg/L in bile; 70 mg/kg in liver; 7 mg/L in urine; 202 mg/kg in gastric contents; and 14 mg/L in vitreous humor. These concentrations were determined using both direct extraction and the method of standard addition. The quantitative results obtained by both procedures were in good agreement. Because of the limited information published on metaxalone toxicity, the pathologist assigned the manner and cause of death as accidental acute hydrocodone intoxication. Four additional cases in which metaxalone was present were analyzed for comparison. Two cases were probable drug-related deaths and had metaxalone aorta blood concentrations of 18 and 11 mg/L. The other two cases had therapeutic metaxalone concentrations in the aortic blood of < 0.75 and 2.1 mg/L.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.030
GPT teacher head0.331
Teacher spread0.301 · 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

Citations17
Published2004
Admission routes1
Has abstractyes

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