New tuberculosis estimates must motivate countries to act
Bibliographic record
Abstract
"In October 2016, the World Health Organization (WHO), released its 2016 Global Tuberculosis (TB) Report [1]. The news, sadly, was not good. The WHO reported that the global TB burden is actually higher than previously estimated. In 2015, there were an estimated 10.4 million new TB cases worldwide [1]. Six countries, namely India, Indonesia, China, Nigeria, Pakistan and South Africa, accounted for 60% of the total burden, with India alone accounting for 27% of the global cases. An estimated 1.8 million people died from TB in 2015, including 0.4 million people that were co-infected with HIV [1]. This means TB kills more people today, than HIV and malaria combined. The WHO report raised concerns about persistent, large gaps in TB case detection and notification. Of the 10.4 million new cases, WHO estimated that only 6.1 million were detected and officially notified in 2015. This left a gap of 4.3 million cases that are considered ‘missing’ – either not diagnosed, or managed outside of the public sector and not notified to TB control programs [1]. For these ‘missing’ cases, there is little data on the quality of TB care they receive, but available evidence suggests that quality of care might be suboptimal [2].[...]"@eng
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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 | 0.028 | 0.109 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.050 | 0.063 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
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".