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Record W2106480878 · doi:10.1136/emermed-2012-201170

A cross-sectional study of emergency department visits by people who inject drugs

2012· article· en· W2106480878 on OpenAlexaffabout
Campbell Aitken, Thomas Kerr, Matthew Hickman, Mark Stoové, Peter Higgs, Paul Dietze

Bibliographic record

VenueEmergency Medicine Journal · 2012
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsAIDS VancouverSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineEmergency departmentSuicide preventionMedical emergencyPoison controlOccupational safety and healthInjury preventionHuman factors and ergonomicsFamily medicineEmergency medicineEnvironmental healthPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: People who inject drugs (PWID) have worse health than non-injectors and are at heightened risk of incidents that necessitate hospital emergency department (ED) visits. STUDY OBJECTIVES: To describe ED visits by PWIDs in Melbourne, Australia, and compare reasons with those given in Vancouver, Canada. METHODS: In 2008-2010, 688 Melbourne PWIDs were interviewed about their ED visits; these data were contrasted with published data about ED visits by PWIDs in Vancouver. RESULTS: Participants reported 132 ED visits in the month preceding interview--27.3% drug-related, 20.5% trauma-related (principally physical assault), 13.6% for psychiatric problems. Melbourne PWIDs are less likely to attend ED for soft-tissue injuries, and more likely to attend after physical assault than PWIDs in Vancouver. CONCLUSION: PWID in Melbourne and Vancouver attend EDs for different reasons; information about PWID visits can help EDs cater for them and provide insights for prevention.

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 score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.056
GPT teacher head0.407
Teacher spread0.351 · 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

Citations15
Published2012
Admission routes2
Has abstractyes

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