Evidence-based, agreed-upon health priorities to remedy the tuberculosis patient's economic disaster
Bibliographic record
Abstract
New literature review of patient costs in tuberculosis reveals the financial burden of the disease http://ow.ly/vBGejRecently, numerous countries have suffered the impact of the worldwide financial crisis [1].Major economic problems have been faced by low and middle income countries; however, even some European Union nations (such as Greece, Spain and Italy) are experiencing the effects of the global crisis [2].Several experts have noted the limited economic resources focused by governments, and international governmental and non-governmental organisations on health systems: dramatic funding reductions for numerous acute and chronic diseases, inability to improve healthcare organisations, incapability to replace personnel leaving their jobs (e.g.migration to a richer country or retirement), and inability to transfer new diagnostic, therapeutic and preventive approaches to daily routine clinical and public health activities.The most relevant outcome of this scenario is the increased burden of some diseases (inaccurate diagnosis and/ or therapy and/or prevention) [3][4][5].The highest risk of a difficult-to-recover picture is associated with increased probability of transmission of infectious diseases.At this point in time it is crucial to develop a strategy of health priorities based on accurately evaluated epidemiological and financial burdens of the most important diseases.Tuberculosis (TB), one of the main global health priorities with about 9 million estimated new cases and 2 million deaths, together with HIV/AIDS and malaria, creates major economic problems in high burden countries and among affected communities [6].Several studies, as well as systematic reviews and metaanalyses, have been carried out on the healthcare burden of TB, including more severe forms of TB such as multidrug-resistant TB (MDR-TB) [7][8][9][10][11][12].The World Health Organization (WHO) and its partners are finalising the latest version of the new post-2015 TB control and elimination strategy, which will be discussed at the World Health Assembly in May 2014 [13,14].With the vision of leaving a TB-free world to future generations (zero deaths, diseases and TB-related suffering) and the goal of putting an end to the global TB epidemic, the new WHO strategy has ambitious targets for 2035 (fig.1): 1) a 95% reduction in TB deaths (compared with 2015); 2) a 90%
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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 | 0.006 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.015 | 0.025 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".