Digital health to end tuberculosis in the Sustainable Development Goals era: achievements, evidence and future perspectives
Bibliographic record
Abstract
Until 2015, the United Nations Millennium Development Goals provided a framework for countries to work towards targets aimed at improving health. Major progress was achieved globally as a result of this drive. Important challenges, however, persist for both communicable ( e.g. tuberculosis (TB) and HIV) and noncommunicable ( e.g. tobacco use) health problems. The Sustainable Development Goals (SDGs), which now guide the global development agenda until 2030, approach these health problems more holistically [1]. The “integrated and indivisible” nature of the SDGs requires action across all layers of society. To achieve the health-specific Goal 3 (“Ensure healthy lives and promote well-being for all at all ages”), countries also need to act on the other 16 SDGs, such as poverty (Goal 1), malnutrition (Goal 2), gender-associated inequalities (Goal 5), investment in information and communications technology and in research by the public and private sectors (Goal 9), transparency, accountability and nondiscriminatory laws (Goal 16), and cross-sectoral collaboration and partnerships (Goal 17). Use of digital technologies to support TB care and prevention can be a model for broader action to achieve the SDGs The authors acknowledge the contribution to the arguments presented in this article of the Global Task Force on digital health for TB , chaired by Giovanni Battista Migliori (WHO Collaborating Centre for TB and Lung Diseases, Maugeri Care and Research Institute, Tradate, Italy), and composed of Andrei Dadu (WHO Regional Office for Europe, Copenhagen, Denmark), Claudia Denkinger (FIND, Geneva, Switzerland), Luis Gustavo do Valle Bastos (Global Drug Facility, Geneva, Switzerland), Richard Garfein (University of California San Diego, San Diego, CA, USA), Richard Lester (University of British Columbia, Vancouver, Canada), Kirankumar Rade (Revised National TB Control Programme, New Delhi, India), Lal Sadasivan (PATH, Washington, DC, USA), Kaiser Shen (USAID, Washington, DC, USA), Alena Skrahina (National TB Programme, Minsk, Belarus), Giovanni Sotgiu (University of Sassari, Sassari, Italy), Alistair Story (Find & Treat, London, UK), Khin Swe Win (Myanmar Medical Association, Yangon, Myanmar), Zelalem Temesgen (Mayo Clinic, Rochester, MN, USA), Bruce V. Thomas (The Arcady Group, Richmond, VA, USA), Kristian van Kalmthout (KNCV Tuberculosis Foundation, The Hague, The Netherlands), Arne von Delft (TB PROOF, Cape Town, South Africa) and Mohammed Yassin (Global Fund to Fight AIDS, TB and Malaria, Geneva, Switzerland). This editorial also draws on some of the discussions at a WHO/ERS consulation held in February 2017 \[18\] (figure 1).
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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 | 0.026 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".