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Record W2177872619 · doi:10.1016/j.ijid.2015.10.001

Sexually transmitted infections and viral hepatitides in patients presenting for non-occupational HIV post-exposure prophylaxis: results of a prospective cohort study

2015· article· en· W2177872619 on OpenAlexaff
Nirojini Sivachandran, Reed Siemieniuk, Pauline Murphy, Andrea Sharp, Christine Walach, Tania Placido, Isaac I. Bogoch

Bibliographic record

VenueInternational Journal of Infectious Diseases · 2015
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity Health NetworkToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineHuman immunodeficiency virus (HIV)CohortViral loadViral hepatitisProspective cohort studyMen who have sex with menImmunologyCartInternal medicineVirology

Abstract

fetched live from OpenAlex

Data evaluating the screening practices for viral hepatitides and sexually transmitted infections (STIs) in patients presenting for non-occupational HIV post-exposure prophylaxis (nPEP) care are limited. Screening practices and prevalences of viral hepatitides and STIs were evaluated in 126 patients presenting to a dedicated HIV prevention clinic for HIV nPEP. Three patients (2.4%) were diagnosed with chronic hepatitis C infection, 28 (22.2%) did not have surface antibodies in sufficient quantity to confer immunity to hepatitis B, and six (4.8%) were diagnosed with an STI. A multivariate regression model did not predict any demographic or clinical features predictive of HBV non-immunity. Beyond screening for HIV infection, evaluation for viral hepatitides and STIs is an important feature in the care of patients presenting for HIV nPEP.

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)0.004
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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.320
Teacher spread0.309 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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