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Record W2300029734

Defining self-reported and prescription measures of adherence to ART: multi-cohort analysis

2013· article· en· W2300029734 on OpenAlexaff
Suzanne M Ingle, Tracy R. Glass, Heidi M. Crane, Robert S. Hogg, M. John Gill, Ammassari Adriana, Michael J. Mugavero, Janet P. Tate, Nicholas Turner, Margaret May, Jonathan A C Sterne

Bibliographic record

VenueBristol Research (University of Bristol) · 2013
Typearticle
Languageen
FieldMedicine
TopicSexual function and dysfunction studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedical prescriptionCohortMedicinePsychologyNursing
DOInot available

Abstract

fetched live from OpenAlex

Background: Early identification of poor adherers to ART could facilitate interventions to prevent treatment failure. However, cohorts collect adherence data in diverse ways and defining levels of adherence is not straightforward. We examined how different measures may be used to identify those at risk of viral failure.<br/><br/>Methods: 6/19 cohorts collaborating in the ART Cohort Collaboration (ART-CC) contributed adherence data: 3 from prescription refills (3 North American cohorts) and 3 from self-report questionnaires (2 European, 1 North American cohort). For prescription data, we derived 1-year percentage adherence and viral suppression (≤500 copies/ml) using the viral load closest to the 1 year time point. For self-report data, we derived percentage adherence in the last 28 days and viral suppression using the closest viral load measure after but within 6 months of the questionnaire. We plotted Receiver Operating Characteristic (ROC) curves to assess discrimination of percentage adherence for diagnosing viral suppression, and estimated the area under the ROC curve (AUROC): 0.5 corresponds to no and 1 to perfect discrimination.<br/><br/>Results: Adherence and viral load data were available from 13276 patients: 9591 and 3685 with prescription and self-report data respectively. Greater proportions of patients were virally suppressed and had ≥95% adherence in cohorts with self-report compared with prescription data. AUROCs varied from 0.56 to 0.85 between cohorts and were systematically higher in cohorts with prescription data, likely due to the categorical rather than continuous nature of the self-report data. <br/><br/>Conclusions: Cohorts were heterogeneous in terms of viral suppression and adherence. Prescription and self-report data measure different aspects of adherence. Self-report items may provide a better snapshot of current adherence enabling identification of inadequate adherence before viral loads begin to climb. This real-time reporting advantage of self-report questionnaires may constitute an intervention which may explain the apparent lower discriminatory power compared with prescription data.<br/>

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)0.001
metaresearch head score (Gemma)0.001
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 score0.064
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.128
GPT teacher head0.341
Teacher spread0.213 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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