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People living with HIV in Estonia: engagement in HIV care in 2013

2016· article· en· W2547429321 on OpenAlexaboutno aff
Kaja-Triin Laisaar, Mait Raag, Irja Lutsar, Anneli Uusküla

Bibliographic record

VenueEurosurveillance · 2016
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersEuropean Regional Development FundEuropean CommissionEesti TeadusagentuurAIDS Healthcare Foundation
KeywordsSerostatusHuman immunodeficiency virus (HIV)MedicineQuarter (Canadian coin)HIV diagnosisViral loadAntiretroviral therapyFamily medicineGerontologyVirologyGeography

Abstract

fetched live from OpenAlex

Estonia had the highest rate of newly diagnosed human immunodeficiency virus (HIV) cases in the European Union (24.6/100,000) and an estimated adult HIV prevalence of 1.3% in 2013. HIV medical care, including antiretroviral therapy (ART), is free of charge for people living with HIV (PLHIV). To maximise the health benefits of HIV treatment, universal access should be achieved. Using data from surveillance and administrative databases and the treatment cascade model, we assessed the number of people infected with HIV, diagnosed with HIV, linked to HIV care, retained in HIV care, on ART, and with suppressed viral load (HIV-RNA: < 200 copies/mL). We identified that about one quarter of the 8,628 HIV-positive people estimated to live in Estonia in 2013 had not been diagnosed with HIV, and another quarter, although aware of their HIV-positive serostatus, had not accessed HIV medical care. Although altogether only 12-15% of all PLHIV in Estonia had achieved viral suppression, the main gap in HIV care in Estonia were the 58% of PLHIV who had accessed HIV medical care at least once after diagnosis but were not retained in care in 2013.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.034
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.014
GPT teacher head0.286
Teacher spread0.271 · 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 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
Published2016
Admission routes1
Has abstractyes

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