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Record W4319982031 · doi:10.3390/v15020490

HIV Drug Resistance in Adults Initiating or Reinitiating Antiretroviral Therapy in Uruguay—Results of a Nationally Representative Survey, 2018–2019

2023· article· en· W4319982031 on OpenAlexfundno aff
Rosa Flieller, Dora Ruchansky, Amalia Girón‐Callejas, María Brasesco, Daniel Barboza Perez, Héctor Chiparelli, Claudia García‐Morales, Daniela Tapia‐Trejo, Jessica Monreal‐Flores, Giovanni Ravasi, Michael R. Jordan, Santiago Ávila‐Ríos

Bibliographic record

VenueViruses · 2023
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS drug development and treatment
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsDolutegravirDarunavirHIV drug resistanceMedicineEfavirenzRitonavirDrug resistanceNevirapineVirologyHuman immunodeficiency virus (HIV)Antiretroviral therapyInternal medicineViral loadMicrobiologyBiology

Abstract

fetched live from OpenAlex

< 0.001) in adults with previous ARV drug exposure (20.3%, 95% CI: 18.7-22.0%) compared to adults without previous ARV drug exposure (12.3%, 11.0-13.8%). HIVDR to any nucleoside reverse transcriptase inhibitors was 10.3% (9.4-11.2%). HIVDR to ritonavir-boosted protease inhibitors was 1.5% (1.1-2.1%); resistance to ritonavir-boosted darunavir was 0.9% (0.4-2.1%) among adults without previous ARV drug exposure and it was not observed among adults with previous ARV drug exposure. Resistance to integrase inhibitors was 12.7% (11.7-13.8%), yet HIVDR to dolutegravir, bictegravir, and cabotegravir was not observed. The high level (>10%) of HIVDR to efavirenz highlights the need to accelerate the transition to the WHO-recommended dolutegravir-based ART. Access to dolutegravir-based ART should be prioritised for people reporting previous ARV drug exposure.

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)0.002
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 score0.217
Threshold uncertainty score0.432

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.060
GPT teacher head0.340
Teacher spread0.280 · 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
Published2023
Admission routes1
Has abstractyes

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