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Record W2467505513 · doi:10.1371/journal.pone.0157512

Drug Toxicity Deaths after Release from Incarceration in Ontario, 2006-2013: Review of Coroner’s Cases

2016· article· en· W2467505513 on OpenAlexaffabout
Emily Groot, Fiona G. Kouyoumdjian, Lori Kiefer, Parvaz Madadi, Jeremy Gross, Brittany Prevost, Reuven Jhirad, Dirk Huyer, Victoria Snowdon, Navindra Persaud

Bibliographic record

VenuePLoS ONE · 2016
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsOffice of the Chief Medical ExaminerUniversity of TorontoPublic Health OntarioMinistry of Community Safety and Correctional ServicesSt. Michael's HospitalQueen's University
Fundersnot available
KeywordsCoronerMedicineDrugPsychological interventionToxicityControlled substancePoison controlEmergency medicineInjury preventionMedical emergencyPharmacologyMedical prescriptionPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: There is an increased risk of death due to drug toxicity after release from incarceration. The purpose of this study was to describe the timing, rate and circumstances of drug toxicity deaths following release from incarceration. This information can be used to help design potential preventive interventions. METHODS AND FINDINGS: We reviewed coroner's files to identify deaths in adults in Ontario between 2006 and 2013 caused by drug toxicity (n = 6,978) and these records were matched with provincial correctional records to identify individuals who died within one year of being released from incarceration (n = 702). Twenty percent (n = 137) of the 702 deaths occurred within one week of release. The majority (77%, n = 538) of deaths after release involved one or more opioids. Of the deaths involving opioids, intervention by another person may have been possible in 318 cases. CONCLUSIONS: Between 2006 and 2013 in Ontario, one in ten drug toxicity deaths in adults occurred within one year of release from provincial incarceration. These findings may help to inform the implemention and assessment of interventions aimed at reducing drug toxicity deaths following release from incarceration.

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 categoriesInsufficient 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 score0.076
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.031
GPT teacher head0.244
Teacher spread0.213 · 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

Citations51
Published2016
Admission routes2
Has abstractyes

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Same venuePLoS ONESame topicOpioid Use Disorder TreatmentFrench-language works237,207