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Record W3094380756 · doi:10.1080/09540121.2020.1837337

Changes in neurocognitive assessment scores after initiating dolutegravir- versus elvitegravir-based antiretroviral therapy

2020· article· en· W3094380756 on OpenAlexaboutno aff
Jessica Adams, Yookyung Christy Choi, Michael A. West, Laura Pontiggia, John Baxter, Jomy George

Bibliographic record

VenueAIDS Care · 2020
Typearticle
Languageen
FieldMedicine
TopicHIV-related health complications and treatments
Canadian institutionsnot available
FundersAmerican Association of Colleges of Pharmacy
KeywordsMontreal Cognitive AssessmentElvitegravirDolutegravirMedicineInterquartile rangeNeurocognitiveInternal medicineTolerabilityRaltegravirCohortAntiretroviral therapyHuman immunodeficiency virus (HIV)Cognitive impairmentCognitionAdverse effectViral loadPsychiatry

Abstract

fetched live from OpenAlex

This prospective cohort study enrolled people living with HIV initiating antiretroviral therapy (ART) containing the integrase inhibitors, dolutegravir (DTG) or elvitegravir (EVG) and administered the Montreal Cognitive Assessment (MoCA) at baseline and again after approximately six months to compare changes in MoCA scores. The proportion of patients found to have cognitive impairment, as indicated by a MoCA score <26/30, on each agent were also compared and comparisons were made between changes in each domain assessed by the MoCA (visuospatial/executive, naming, attention, language, abstraction, delayed recall, and orientation). Thirty-five evaluable participants were enrolled, 18 on DTG and 17 on EVG. The median [interquartile range(IQR)] age was 44 (32 to 54) years, 63% were male, 57% were African American. The median (IQR) MoCA score at baseline was 25 (23 to 27) with no difference between groups (p=0.249). The median (IQR) change in MoCA score was 0 (−1 to 2) for DTG and 1 (0 to 3) for EVG (p = 0.183). Of those on DTG, 8 (44%) had MoCA scores <26 on follow-up compared to 11 (65%) on EVG (p = 0.229). There were no significant differences in changes in any of the individual MoCA domains.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.047
GPT teacher head0.353
Teacher spread0.306 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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