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Record W3030643727 · doi:10.2147/cia.s252849

<p>Opioid Poisoning and Opioid Use Disorder in Older Trauma Patients</p>

2020· article· en· W3030643727 on OpenAlexaffabout
Raoul Daoust, Jean Paquet, Lynne Moore, Alexis Cournoyer, Marcel Émond, Sophie Gosselin, Gilles Lavigne, Jean‐Marc Mac‐Thiong, Jean‐Marc Chauny

Bibliographic record

VenueClinical Interventions in Aging · 2020
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsCanadian Sleep & Circadian NetworkCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre Hospitalier de l’Université de MontréalCentre intégré de santé et de services sociaux de Chaudière-AppalachesCentre Intégré de Santé et de Services Sociaux des LaurentidesSanté MontérégieUniversité de MontréalUniversité LavalFonds de Recherche du Québec - SantéMcGill University Health CentreHôpital du Sacré-Cœur de Montréal
Fundersnot available
KeywordsMedicineOpioidRetrospective cohort studyHazard ratioOpioid use disorderMedical prescriptionInternal medicineCohortPoison controlCohort studyAnesthesiaPediatricsEmergency medicineConfidence intervalPharmacology

Abstract

fetched live from OpenAlex

BACKGROUND: Patients hospitalized following a traumatic injury will be frequently treated with opioids during their stay and after discharge. We examined the relationship between acute phase (<3 months) opioid use after discharge and the risk of opioid poisoning or use disorder in older trauma patients. METHODS: In a retrospective multicenter cohort study conducted on registry data, we included all patients ≥65 years admitted (hospital stay >2 days) for injury in 57 trauma centers in the province of Quebec (Canada) between 2004 and 2014. We searched for opioid poisoning and opioid use disorder from ICD-9 to ICD-10 code diagnosis after their initial injury. Patients that filled an opioid prescription within a 3-month period after sustaining the trauma were compared to those who did not, using Cox proportional hazards regressions. RESULTS: A total of 70,314 admissions were retained for analysis; median age was 82 years (IQR: 75-87), 68% were women, and 34% of the patients filled an opioid prescription within 3 months of the initial trauma. During a median follow-up of 2.6 years (IQR: 1-5), 192 participants (0.27%; 95% CI: 0.23%-0.31%) were hospitalized for opioid poisoning and 73 (0.10%; 95% CI: 0.08%-0.13%) were diagnosed with opioid use disorder. Having filled an opioid prescription within 3 months of injury was associated with an increased hazard ratio of opioid poisoning (2.8; 95% CI: 2.1-3.8) and opioid use disorder (4.2; 95% CI: 2.4-7.4) after the injury. However, history of opioid poisoning (2.6; 95% CI: 1.1-5.8), of substance use disorder (4.3; 95% CI: 2.4-7.7), or of the opioid prescription filled (2.8; 95% CI: 2.2-3.6) before the trauma, was also related to opioid poisoning or opioid use disorder after the injury. CONCLUSION: Opioid poisoning and opioid use disorder are rare events after hospitalization for trauma in older patients. However, opioids should be used cautiously in patients with a history of substance use disorder, opioid poisoning or opioid use.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.297
Threshold uncertainty score0.590

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.070
GPT teacher head0.382
Teacher spread0.312 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2020
Admission routes2
Has abstractyes

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