Feasibility and Effectiveness of Mobile App for Active Case Finding for Tuberculosis in India
Bibliographic record
Abstract
Background: Tuberculosis (TB) is an infectious disease with 2.8 million cases and 480,000 deaths each year in India. The city of Indore alone with a population of 3.5 million had 7,839 identified TB cases in 2017. However, about two to three thousand additional cases remain unidentified per district officials. The unidentified cases lead to an endemic TB and hamper the efforts of organizations such as The Collaborative to Eliminate TB from India (CETI) to reduce the incidence of TB with the method of Active Case Finding (ACF).1 Previously, 1,332 mobile apps attempted to use technology to overcome the challenge of unreported TB patients in Indian slum areas due to the inaccurate, lost, or unhelpful data collected in ACF; yet the existing apps for TB prevention and treatment possessed minimal functionality. Over a period of 3 months, the CETI developed a mobile data collection app to generate a TB diagnostic survey and to collect data from patient registration form. Methods: To study the feasibility and effectiveness of the app, a pilot survey was conducted of 163,496 homes covering a population of 828,020 in the slum areas of Indore and Bhopal. Findings: Between the years of 2018 and 2019, 14,349 pulmonary suspected cases and 4,357 extra pulmonary suspected cases of TB were identified. Among the total of 18,706 cases identified, 7,756 patients (48.1%) had low-grade fever for over 2 weeks, 6,331 patients (39.2%) had persistent cough for more than 2 weeks, 7,693 patients (47.7%) had weight loss, and 251 patients (1.6%) had cough with blood. Interpretation: This pilot experience shows that an app is a useful tool for TB case recording and follow-up in the field. Further training of the health workers, and more widespread availability and ease of use of mobile phones will be necessary.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".