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Record W4234237116 · doi:10.22374/cjgim.v10i1.20

HIV Testing: Support for Routine Screening

2015· article· en· W4234237116 on OpenAlexaffvenue
BSc. Pharm Brett Edwards

Bibliographic record

VenueCanadian Journal of General Internal Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineHIV screeningHuman immunodeficiency virus (HIV)Task forceHIV diagnosisDisease controlFamily medicineTransmission (telecommunications)Intensive care medicineAntiretroviral therapyViral loadMen who have sex with menEnvironmental health

Abstract

fetched live from OpenAlex

Summary This article discusses the recent evolution of human immunodeficiency virus (HIV) screening recommendations with significantly expanded role for routine HIV testing. After the Centre for Disease Control (CDC) released recommendations for routine screening in 2006, it was anticipated that the United States Preventive Services Task Force (USPSTF), a national body charged with providing evidence-based recommendations for preventive services, would follow shortly. However, they refrained, citing a lack of evidence at the time to make such a recommendation, and maintained a recommendation for risk-based screening. Following an analysis of recent literature, in 2013 the USPSTF finally made a recommendation for routine HIV screening on the grounds of new evidence. The recommendations are based on the clinical benefit, the failures of risk-based screening, cost-effectiveness data with reduction in HIV related morbidity/mortality, and lower rates of transmission. This article highlights some of the literature that accounted for the change in recommendations and provides a basic review of HIV testing techniques available to the internists and the recommendations for routine screening of patients.

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)0.016
metaresearch head score (Gemma)0.174
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.174
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0230.004

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 head0.133
GPT teacher head0.376
Teacher spread0.243 · 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 designNot applicable
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
Published2015
Admission routes2
Has abstractyes

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