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Record W4413433674 · doi:10.1093/cid/ciaf467

Characterizing Treatment Adherence Trajectories in the endTB Multisite Cohort of Drug-Resistant Tuberculosis Patients: An Application of Group-Based Trajectory Modeling

2025· article· en· W4413433674 on OpenAlexafffund
Stephanie Law, Isabel Fulcher, Samreen Ashraf, Mathieu Bastard, Wisny Docteur, Molly F. Franke, Dalia Guerra, Catherine Hewison, Helena Huerga, Munira Khan, Palwasha Khan, Uzma Khan, Jarmila Klieščiková, Andargachew Kumsa Erena, Nino Lomtadze, Fauziah Asnely Putri, Michael Rich, Kwonjune J. Seung, Alena Skrahina, Meseret Tamirat, Luan Nguyen Quang Vo, Carole D. Mitnick

Bibliographic record

VenueClinical Infectious Diseases · 2025
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersUnitaidHarvard Data Science Initiative, Harvard UniversityCanadian Institutes of Health ResearchMédecins Sans Frontières
KeywordsMedicineObservational studyTuberculosisInternal medicineCohortRetrospective cohort studyReceiver operating characteristicPathology

Abstract

fetched live from OpenAlex

BACKGROUND: In tuberculosis (TB) care, adherence is often assessed using a simple 80% threshold, which may overlook meaningful patterns. We analyzed adherence trajectories among individuals treated for rifampicin- or multidrug-resistant TB (RR/MDR-TB) in the endTB observational study to identify more informative patterns. METHODS: We applied a joint latent class mixed model to classify adherence trajectories and assess their relationship with treatment outcomes. Model performance was compared to common classification methods (eg 80% adherence threshold) using Kendall's τb and area under the receiver operating curve for predicting unsuccessful outcomes. RESULTS: Among 1787 individuals, we identified 4 adherence patterns: "consistently high" (72.5%), "high to low" (14.3%), "low to high" (7.3%), and "consistently low" (5.9%). Compared to the "consistently high" group, those in "high to low" (hazard ratio [HR] = 23.2; 95% confidence interval [CI]: 15.7-24.3) and "consistently low" (HR = 43.2; 95% CI: 26.2-71.5) groups had significantly higher risk of unsuccessful outcomes, while the "low to high" group did not (HR = 0.7; 95% CI: .1-3.8). Our trajectory model more accurately predicted outcomes than common classification methods (P < .01). CONCLUSIONS: Group-based trajectory modeling provides more nuanced insights into adherence patterns than conventional classification methods. Our findings demonstrate that patients with RR/MDR-TB who exhibited initial poor adherence followed by subsequent improvement achieved clinical outcomes comparable to those with consistently high adherence throughout treatment. This finding challenges the prevailing assumption that sustained high adherence is necessary for treatment success, suggesting that adherence patterns, rather than overall adherence rates, may be more predictive of clinical outcomes in the management of RR/MDR-TB.

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)0.007
metaresearch head score (Gemma)0.016
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 score0.018
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.036
GPT teacher head0.376
Teacher spread0.341 · 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

Citations4
Published2025
Admission routes2
Has abstractyes

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