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Record W2592031711 · doi:10.9778/cmajo.20160013

Fatal overdoses involving hydromorphone and morphine among inpatients: a case series

2017· article· en· W2592031711 on OpenAlexaffvenueabout
Amanda Lowe, Michael A. Hamilton, Julie Greenall BScPhm MHSc, Jessica Ma, Irfan A. Dhalla, Nav Persaud

Bibliographic record

VenueCMAJ Open · 2017
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsOffice of the Chief Medical ExaminerUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsHydromorphoneMedicineMorphineEmergency medicinePsychological interventionMedical emergencyNarcoticOpioidIntervention (counseling)Intensive care medicineAnesthesiaPediatricsInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Opioids have narrow therapeutic windows, and errors in ordering or administration can be fatal. The purpose of this study was to describe deaths involving hydromorphone and morphine, which have similar-sounding names, but different potencies. METHODS: In this case series, we describe deaths of patients admitted to hospital or residents of long-term care facilities that involved hydromorphone and morphine. We searched for deaths referred to the Patient Safety Review Committee of the Office of the Chief Coroner for Ontario between 2007 and 2012, and subsequently reviewed by 2014. We reviewed each case to identify intervention points where errors could have been prevented. RESULTS: We identified 8 cases involving decedents aged 19 to 91 years. The cases involved errors in prescribing, order processing and transcription, dispensing, administration and monitoring. For 7 of the 8 cases, there were multiple (2 or more) possible intervention points. Six cases may have been prevented by additional patient monitoring, and 5 cases involved dispensing errors. INTERPRETATION: Opioid toxicity deaths in patients living in institutions can be prevented at multiple points in the prescribing and dispensing processes. Interventions aimed at preventing errors in hydromorphone and morphine prescribing, administration and patient monitoring should be implemented and rigorously evaluated.

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 score0.005
Threshold uncertainty score0.662

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 communication0.0000.001
Open science0.0000.001
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 head0.033
GPT teacher head0.297
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

Citations13
Published2017
Admission routes3
Has abstractyes

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