Tuberculosis: 12. Global disease and the role of international collaboration.
Bibliographic record
Abstract
The horrendous impact of the HIV epidemic, including its pathophysiologi- cal, clinical and epidemiological interaction with TB, 3 has been most evident in sub-Saharan Africa (particularly eastern and southern Africa) and is rapidly becom- ing apparent in India and Thailand. Estimates of the global impact of TB indicate that TB is the most frequent cause of death in the world from a single agent in young adults 1 and that at least 20 million people have died unnecessarily of this dis- ease in the past decade. These facts necessitate international collaboration in inter- ventions to deal with TB. In this paper I present an overview of global interventions to eliminate TB, focusing in particular on the roles of scientific and technical ex- perts, government agencies, and voluntary associations in these cooperative efforts. The objective of intervention To be effective, global action on TB must have clear objectives. Ultimately, the aim of any interventions should be elimination of the disease. Global elimination of an infectious disease has been achieved only once before, for smallpox, but there are divergent views as to whether this goal can be accomplished for TB. The following characteristics facilitated the elimination of smallpox: an effective vaccination strategy, no natural reservoir outside humans and no carrier state for the virus. TB does not have these characteristics. Instead, the prevention strategy is based on case management, there are animal reservoirs of the bacteria and most in- fected people carry viable bacilli without symptoms. On what grounds, then, can we hope that TB can be eliminated? Models of intervention
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.067 | 0.026 |
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".