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Record W2793176791 · doi:10.1016/j.jana.2018.02.004

Nurse-Led HIV PEP Program Used by Men at High Risk for HIV Seroconversion

2018· article· en· W2793176791 on OpenAlexafffundabout
Patrick O’Byrne, Paul MacPherson, Lauren Orser

Bibliographic record

VenueJournal of the Association of Nurses in AIDS Care · 2018
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsOttawa Public HealthOttawa Hospital
FundersOntario HIV Treatment Network
KeywordsMen who have sex with menMedicineSeroconversionHuman immunodeficiency virus (HIV)SyndemicTransmission (telecommunications)Post-exposure prophylaxisPopulationFamily medicineEnvironmental healthDemographySyphilis

Abstract

fetched live from OpenAlex

We trialed a nurse-led HIV postexposure prophylaxis (PEP) program in two sexually transmitted infection clinics in Ottawa, Canada. From September 5, 2013 to September 4, 2015, 112 persons sought PEP: 103 were male, of whom 84 were men who have sex with men (MSM). Seventy-two patients (59 MSM) initiated PEP; 11 were diagnosed with HIV: 6 diagnoses occurred during initial assessment (all MSM; 1 also shared injection equipment); 5 MSM were diagnosed with HIV within 1 year of seeking PEP. This level of positivity indicated that, when access is facilitated, individuals at high risk of HIV seek PEP. However, the 8.5% of MSM who seroconverted within a year of taking PEP demonstrated that this group remained at risk and needed additional prevention services. Delivery of PEP should include provision of medication, as well as an opportunity to address individual-level HIV risk strategies and population-level syndemic conditions that contribute to ongoing HIV transmission among MSM.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.317

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.009
GPT teacher head0.331
Teacher spread0.322 · 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

Citations24
Published2018
Admission routes3
Has abstractyes

Explore more

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