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Record W1864065351 · doi:10.1111/1556-4029.12972

Forensic Investigation of Methadone Concentrations in Deceased Breastfed Infants

2015· article· en· W1864065351 on OpenAlexaff
Parvaz Madadi, Lauren E. Kelly, Colin J.D. Ross, Charis Kepron, James N. Edwards, Gideon Koren

Bibliographic record

VenueJournal of Forensic Sciences · 2015
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsOffice of the Chief Medical ExaminerChild and Family Research InstituteWestern UniversityAdvantage Forensics (Canada)Hospital for Sick ChildrenUniversity of British ColumbiaHealth Sciences Centre
Fundersnot available
KeywordsMethadoneMedicineForensic toxicologyAutopsyMedical examinerBreastfeedingCause of deathMethadone maintenancePediatricsAnesthesiaPoison controlEmergency medicineInjury preventionInternal medicineDisease

Abstract

fetched live from OpenAlex

There is a paucity of data to aid in assessing whether postmortem methadone findings in breastfed infants are clinically and/or toxicologically significant. Two cases are reported in which methadone was detected in deceased neonates whose mothers were enrolled in methadone maintenance programs and were breastfeeding. In addition to a complete autopsy and toxicological testing for alcohol, prescription medications, and drugs of abuse, pharmacogenetic analysis was performed for variants in genes related to methadone metabolism and response. In both cases, the postmortem methadone concentration measured in neonatal heart blood was higher than the maximum serum methadone concentration reported in living breastfed infants whose mothers were receiving methadone. However, additional analysis of antemortem blood indicated postmortem redistribution of methadone. Pharmacogenetic results were suggestive of a potential predisposition to methadone toxicity based on studies in adults; the significance of these findings in breastfed neonates requires further research. The medical cause of death was unascertained in both cases.

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)0.002
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 score0.286
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.044
GPT teacher head0.305
Teacher spread0.261 · 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

Citations14
Published2015
Admission routes1
Has abstractyes

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