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Record W2128813804 · doi:10.1186/1752-1505-2-8

HIV transmission as a result of drug market violence: a case report

2008· article· en· W2128813804 on OpenAlexafffund
Will Small, Thomas Kerr, Evan Wood

Bibliographic record

VenueConflict and Health · 2008
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsUniversity of British ColumbiaSt. Paul's Hospital
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsMedicineTransmission (telecommunications)Public healthSexual transmissionDrugEnvironmental healthPsychiatryHuman immunodeficiency virus (HIV)Medical emergencyFamily medicineNursing

Abstract

fetched live from OpenAlex

While unprotected sexual intercourse and the use of contaminated injection equipment account for the majority of HIV infections worldwide, other routes of HIV transmission have received less attention. We report on a case of HIV transmission attributable to illicit drug market violence involving a participant in a prospective cohort study of injection drug users. Data from a qualitative interview was used in addition to questionnaire data and nursing records to document an episode of violence which likely resulted in this individual acquiring HIV infection. The case report demonstrates that the dangers of drug market violence go beyond the immediate physical trauma associated with violent altercations to include the possibility for infectious disease transmission. The case highlights the need to consider antiretroviral post-exposure prophylaxis in cases of drug market violence presenting to the emergency room, as well strategies to reduce violence associated with street-based drug markets.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0080.003
Scholarly communication0.0030.003
Open science0.0020.004
Research integrity0.0080.006
Insufficient payload (model declined to judge)0.0030.001

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 head0.076
GPT teacher head0.392
Teacher spread0.316 · 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 designCase report
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

Citations0
Published2008
Admission routes2
Has abstractyes

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