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Record W4313280149

Related factors to non-adherence to antiretroviral therapy in HIV/AIDS patients.

2022· article· en· W4313280149 on OpenAlexaff
Juan Andrés Arrieta-Martínez, J.I. Estrada Acevedo, Carlos A. Macías Gomez, Juliana Madrigal-Cadavid, Juan Alberto Serna, Paulo Andrés Giraldo, Oscar Iván Quirós Gómez

Bibliographic record

VenuePubMed · 2022
Typearticle
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsHelix Biopharma (Canada)
Fundersnot available
KeywordsMedicineAntiretroviral treatmentLogistic regressionRetrospective cohort studyPopulationObservational studyAdverse effectAntiretroviral therapyHuman immunodeficiency virus (HIV)PediatricsInternal medicineViral loadImmunologyEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To identify sociodemographic, clinical, and pharmacological factors associated with nonadherence to antiretroviral treatment in patients with human immunodeficiency virus/acquired immunodeficiency syndrome treated between 2017 and 2020 in four cities in Colombia. METHOD: An observational, cross-sectional, retrospective study was conducted of a population of patients with human immunodeficiency virus/acquired immunodeficiency syndrome treated between 2017 and 2020. The Morisky-Green scale, the simplified medication adherence questionnaire, and the simplified scale to detect adherence problems to antiretroviral treatment were applied to determine patient adherence. A binomial multiple logistic regression was performed to evaluate the factors that best explain nonadherence. RESULTS: A total of 9,835 patients were evaluated, of whom 74.4% were men, 71.1% were aged between 18 and 44 years, 76.0% had attended at most secondary school, 78.1% were single, and 97.6% resided in an urban area. After applying three different scales to each patient, 10% of the study population were identified as nonadherent to treatment. The risk of nonadherence was significantly higher in patients who presented any drug- related problem or had an adverse reaction to antiretroviral drugs. CONCLUSIONS: The variables most strongly associated with nonadherence to antiretroviral treatment were drug-related problems, adverse drug reactions, a history of nonadherence to treatment, and psychoactive substance use.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.269
Teacher spread0.229 · 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.

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

Citations2
Published2022
Admission routes1
Has abstractyes

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