Bibliographic record
Abstract
At long last, tuberculosis (TB) is getting the political attention that it deserves, being the leading infectious killer of humans today.In 2016, there were 10.4 million estimated new TB cases, with over 1.7 million deaths [1].The G20 declaration of July 2017 included TB in the context of the need to respond to the antimicrobial resistance threat, following the group's meeting in Hamburg, Germany [2].In November 2017, for the first time, a WHO Global Ministerial Conference on TB was held in Moscow, Russia, culminating in the Moscow Declaration to End TB [3].This year, in September, the United Nations General Assembly (UNGA) will hold the first-ever high-level meeting on the fight against TB [4].While the political attention brings much needed hope, other developments provide cause for worry.The United States government, the largest funder of TB control and research, is rapidly scaling back on overseas aid, slashing billions from global health and humanitarian assistance [5].Canada, despite its progressive policies, is spending substantially less on international aid than comparable G7 countries [6].And while there are considerable uncertainties, Brexit could have a major impact on European Union international development and humanitarian policies and is expected to challenge the EU's role as the world's leading donor [7].The case for funding global health in general, and TB in particular, in this political climate will lack for attention as long as wealthy donor countries focus their priorities on populist and nationalist demands or short-term outcomes of a transactional nature.It is therefore critical for countries most affected by TB to step up, show leadership, and invest in TB control as well as research.Take the case of Brazil, Russia, India, China, and South Africa (BRICS), which together account for 46% of all incident cases of TB and 40% of all TB-related mortality [1].With strong, if uneven, economic growth in BRICS, and their growing stature and leadership in the political arena, these countries are well placed to lead the charge against a disease that is a leading killer of their citizens and a huge drain on their economies [8].In fact, investments in TB control can lead to a huge return on investments for these countries [9].Commendably, there are signs of the BRICS stepping up to deal with TB, commensurate with their disease burden and economic and scientific prowess [8].The BRICS Leaders Xiamen Declaration (2017) specifically mentioned the need to improve surveillance of TB and also agreed to set up a TB research network [10].In fact, the BRICS are now major producers of TB research [11].While the US remains the top producer of TB research in the past 2 decades, India and China have emerged as the second and third leading producers of TB research in recent years [11].Further, bibliometric analyses show that the average year-on-year increase in TB publications from the BRICS countries was, in the past decade, nearly double the overall year-on-year increase across all countries [11].
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.013 | 0.024 |
| Insufficient payload (model declined to judge) | 0.039 | 0.025 |
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".