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Record W1567420631 · doi:10.2174/1874613601004010076

The Development and Implementation of an Outreach Program to Identify Acute and Recent HIV Infections in New York City

2010· article· en· W1567420631 on OpenAlexfundno aff
Richard Silvera, Dylan Stein, Richard Hutt, Robert Hagerty, Demetre Daskalakis, Fred Valentine, Michael Marmor

Bibliographic record

VenueThe Open AIDS Journal · 2010
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesCenter for AIDS Research, University of WashingtonNational Institutes of HealthSchool of Medicine, New York UniversityYork University
KeywordsOutreachMedicineHuman immunodeficiency virus (HIV)Metropolitan areaThe InternetMass mediaFamily medicineAdvertisingWorld Wide WebEconomic growthBusinessPathologyComputer science

Abstract

fetched live from OpenAlex

INTRODUCTION: Since 2004, the authors have been operating First Call NYU, an outreach program to identify acute and recent HIV infections, also called primary HIV infections, among targeted at-risk communities in the New York City (NYC) metropolitan area. MATERIALS AND METHODOLOGY: First Call NYU employed mass media advertising campaigns, outreach to healthcare providers in NYC, and Internet-based efforts including search engine optimization (SEO) and Internet-based advertising to achieve these goals. RESULTS: Between October 2004 and October 2008, 571 individuals were screened through this program, leading to 446 unique, in-person screening visits. 47 primary HIV infections, including 14 acute and 33 recent HIV infections, were identified. DISCUSSION: Internet and traditional recruitment methods can be used to increase self-referrals for screening following possible exposure to HIV. CONCLUSION: Community education of at-risk groups, with the goal of increased self-diagnosis of possible acute HIV infection, may be a useful addition to traditional efforts to identify such individuals.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.935
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.066
GPT teacher head0.473
Teacher spread0.407 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations14
Published2010
Admission routes1
Has abstractyes

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