The role of digital health technology in tuberculosis control: a systematic literature review and comparative analysis based on the WHO guidelines
Bibliographic record
Abstract
This study presents a systematic review with the objective of evaluating the utilisation of digital technology in the surveillance of Tuberculosis (TB) within the context of public health, and of determining the extent to which this practice aligns with the World Health Organization (WHO) recommendation. The methodology was divided into two distinct phases. In the initial phase, a systematic literature review was conducted utilising the Prism and the Parsifal tool. Subsequently, the digital technologies identified in the selected articles were analysed in accordance with WHO documentation and a patient-centred approach. From an initial pool of 2090 articles, nine studies were meticulously selected, including impactful research from regions such as India, China, Uganda, Sudan, Indonesia, Ukraine, Tanzania, South Africa an Philippines. These studies demonstrate that digital technologies have a beneficial impact. The digital technologies that have been highlighted as offering the most promising advancements in the field of TB surveillance, addressing existing challenges and integrating digital solutions seamlessly into TB control programmes, remain pivotal. In order to eradicate TB as a global health threat, future endeavours must focus on refining digital interventions, overcoming barriers and ensuring equitable access.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".