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Record W4213423896 · doi:10.3390/jpm12030334

Patient and Physician Preferences for Regimen Attributes for the Treatment of HIV in the United States and Canada

2022· article· en· W4213423896 on OpenAlexaffabout
Heather L. Gelhorn, Cindy Garris, Erin Arthurs, Frank Spinelli, Katelyn Cutts, Gin Nie Chua, Hannah Collacott, Bertrand Lebouché, Erik Lowman, Howard Rice, Sebastian Heidenreich

Bibliographic record

VenueJournal of Personalized Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsMcGill UniversityMcGill University Health CentreGlaxoSmithKline (Canada)
Fundersnot available
KeywordsHuman immunodeficiency virus (HIV)RegimenFamily medicineMedicineInternal medicine

Abstract

fetched live from OpenAlex

A long-acting injectable (LAI) antiretroviral therapy (ART) regimen is now available as a treatment option for virologically suppressed adults with HIV-1. This study assessed preference for a LAI regimen using an online survey of virally suppressed people living with HIV (PLWH) and physicians treating HIV in the US and Canada. Preference was elicited in a discrete choice experiment (DCE) with three choice options (switch to a LAI regimen, switch to another daily oral ART regimen, or stay on their current daily oral ART regimen) and four treatment attributes. A total of 553 PLWH and 450 physicians completed the survey. From the DCE results, 59% of PLWH were predicted to prefer a LAI over an alternative oral ART or staying on their current oral treatment, and 55-66% of physicians were predicted to recommend LAI for PLWH, depending on the treatment challenge scenario presented. PLWH indicated LAI would remove daily reminders of HIV (75%) and reduce feelings of being stigmatized (68%). A majority of PLWH and physicians preferred a LAI over oral ART to overcome treatment challenges such as daily pill burden and adherence. These benefits of LAI ART along with preferences of PLWH and physicians can help to inform ART choice.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.971

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 head0.334
Teacher spread0.287 · 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 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

Citations15
Published2022
Admission routes2
Has abstractyes

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