Bibliographic record
Abstract
In spite of significant progress made in tuberculosis (TB) control, nine million people developed TB disease in 2013, and 1.5 million died of TB1. While implementation of the Stop TB (DOTS) Strategy has cured millions of patients with TB, and undoubtedly saved lives, the impact of this strategy on reducing TB incidence has been disappointing1. The TB epidemic is declining at the rate of 1.5 per cent per year, much slower than what mathematical models had predicted1. At the current rate of decline, TB elimination by 2050 is considered impossible. DOTS, apparently, cures patients and saves lives, but it does not seem to be very effective in interrupting TB transmission. Under India's Revised National TB Control Programme (RNTCP) millions of TB patients have been treated, and countless lives have been saved2,3. But TB incidence in India continues to remain high3. Of the nine million TB cases in 2013, India alone accounted for 25 per cent of the cases. India also accounts for one of the three million ‘missing’ cases-patients with TB who are either not diagnosed, or not notified1,4. In 2014, the World Health Assembly endorsed a new, bold plan called “The End TB Strategy”5. The vision is “A world free of TB - Zero TB deaths, Zero TB disease, and Zero TB suffering”. The goal is to end the global TB epidemic (<10 cases per 100,000). Essential elements of the three pillars of this strategy are shown below. How is the ‘End TB Strategy’ relevant in the Indian context, and how can India be a world leader in implementing the End TB Strategy?
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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 | 0.116 | 0.184 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.002 | 0.027 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".