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Record W4300126253 · doi:10.17615/9njc-7b37

Predictive Accuracy of the Veterans Aging Cohort Study Index for Mortality With HIV Infection: A North American Cross Cohort Analysis

2020· article· en· W4300126253 on OpenAlexfundno aff
Ronald J. Bosch, James J. Goedert, Gregory D. Kirk, Marina B. Klein, Robert S. Hogg, Benigno Rodríguez, Stephen J. Gange, Kathryn Anastos, Sharada P. Modur, Anita Rachlis, Jennifer E. Thorne, Kate Buchacz, Michael A. Horberg, James H. Willig, Lisa P. Jacobson, Sean B. Rourke, Janet P. Tate, Mari M. Kitahata, Kelly A. Gebo, M. John Gill, Joseph J. Eron, Timothy R. Sterling, Sonia Napravnik, Steven G. Deeks, Amy C. Justice, John T. Brooks, Richard D. Moore, Keri N. Althoff

Bibliographic record

VenueUNC Libraries · 2020
Typearticle
Languageen
FieldMedicine
TopicHIV-related health complications and treatments
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteNational Institute on AgingDeutsches KrebsforschungszentrumNational Institute on Alcohol Abuse and AlcoholismNational Institutes of HealthCanadian Institutes of Health ResearchCenters for Disease Control and Prevention
KeywordsCohortHuman immunodeficiency virus (HIV)MedicineCohort studyGerontologyDemographyIndex (typography)Environmental healthInternal medicineVirologyComputer science

Abstract

fetched live from OpenAlex

By supplementing an index composed of HIV biomarkers and age (Restricted Index) with measures of organ injury, the Veterans Aging Cohort Study (VACS) Index more completely reflects risk of mortality. We compare the accuracy of the VACS and Restricted Indices 1) among subjects outside the Veterans Healthcare System (VA), 2) over 1–5 years of prior exposure to antiretroviral therapy (ART), and 3) within important patient subgroups.

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)0.004
metaresearch head score (Gemma)0.008
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 score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.025
GPT teacher head0.320
Teacher spread0.296 · 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

Citations16
Published2020
Admission routes1
Has abstractyes

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Same venueUNC LibrariesSame topicHIV-related health complications and treatmentsFrench-language works237,207