Costs of Digital Adherence Technologies for Tuberculosis Treatment Support, 2018–2021
Bibliographic record
Abstract
A s part of the mission to cure and ultimately elim- inate tuberculosis (TB), maintaining treatment adherence poses a substantial barrier (1).Persons with TB must complete multidrug regimens typically lasting >6 months.Even small lapses in adherence can be associated with poorer treatment outcomes, including relapse with the potential for further transmission (2).TB prevention and care programs have often sought to improve adherence, and hence treatment outcomes, by using directly observed therapy (DOT) (3,4).However, healthcare system barriers (mostly resource limitations), coupled with stigma, loss of autonomy, and the heavy burden of DOT clinic visits, can result in subpar outcomes and adherence that may not exceed that of self-administered treatment (5-8).Those limitations have led the World Health Organization (WHO) to recommend community or home-based DOT over healthcare facility-based DOT or unsupervised treatment (4).WHO defines a DOT provider as any person who observes the person with TB taking their medications in real time (4).By leveraging current advances in mobile technologies, person-centered treatment observation can be achieved by digital adherence technologies (DATs) such as medication sleeves, smart pill boxes, and video-supported therapy.Moreover, real-time digital adherence information offers the possibility of tailoring treatment support to individual needs.However, before TB programs adopt those technologies as a central strategy for treatment support, evidence for their effectiveness must be robust.Demonstration projects highlighting feasibility and acceptability of DATs for TB treatment support provide substantial evidence; to date, evidence is more limited for clinical outcomes with use of DATs than for other forms of treatment observation or self-administered treatment (9-11).In principle, DATs can enable expansion of TB treatment supervision and support while reducing the burden on persons with TB and their providers.Information about the cost to TB programs of those technologies and their real-world cost-effectiveness comes largely from pilot and modeling studies (11,12).To estimate the cost of 2 DATs currently recommended for use by WHO (4), we used data from Costs of Digital Adherence Technologies for Tuberculosis Treatment Support, 2018-2021
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".