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Record W1882529678 · doi:10.1111/1556-4029.12807

Parent and Metabolite Opioid Drug Concentrations in Unintentional Deaths Involving Opioid and Benzodiazepine Combinations<sup>,</sup><sup>,</sup>

2015· article· en· W1882529678 on OpenAlexaff
Marcia D. Fields, Marie A. Abate, Lan Hu, D. Leann Long, Matthew L. Blommel, Nabila A. Haikal, James C. Kraner

Bibliographic record

VenueJournal of Forensic Sciences · 2015
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsOffice of the Chief Medical ExaminerHealth Sciences North
FundersNational Institute of General Medical SciencesCenters for Disease Control and Prevention
KeywordsHydrocodoneOxycodoneOxymorphoneMetaboliteMethadoneOpioidAlprazolamHydromorphonePharmacologyFentanylDiazepamMedicineAnesthesiaChemistryInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Effects of benzodiazepines on postmortem opioid parent and parent/metabolite blood concentration ratios were determined for fentanyl-, hydrocodone-, methadone-, or oxycodone-related accidental deaths. These opioids are partially metabolized by the CYP3A4 enzyme system, which is also affected by diazepam and alprazolam. Opioid/metabolite combinations examined were as follows: fentanyl/norfentanyl, hydrocodone/dihydrocodeine, methadone/EDDP, and oxycodone/oxymorphone. Parent opioid concentrations were analyzed for 877 deaths. Parent/metabolite concentration ratios were analyzed for 349 deaths, excluding cases with co-intoxicants present known to interfere with opioid elimination. Alprazolam in combination with diazepam significantly decreased median hydrocodone concentrations by 48% (p = 0.01) compared to hydrocodone alone. The methadone parent/metabolite concentration ratio was reduced by 35% in the presence of diazepam compared to methadone alone (p = 0.03). Benzodiazepines did not statistically significantly affect fentanyl or oxycodone concentrations. Possible factors affecting opioid concentrations and possible toxicity development, including any differential effects on specific opioids, should continue to be explored.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.716

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.036
GPT teacher head0.299
Teacher spread0.264 · 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
Published2015
Admission routes1
Has abstractyes

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