From pests to tests: training rats to diagnose tuberculosis
Bibliographic record
Abstract
2020 is the year of the rat. The rat is the first of the 12 Chinese zodiac signs, and represents spirit, alertness, flexibility and vitality. In respiratory medicine, we may think of rats as vectors for diseases, such as pulmonary forms of hantavirus disease or leptospirosis, and pneumonic plague. Rodent control is thus part of hygiene guidelines and the International Health Regulations. And yet, the rat's keen sense of smell has led to its incredible career as a living tuberculosis (TB) detector. It's time… to find and treat all patients with TB. Rats may have a say in research towards better diagnostic tests. The authors thank all study participants, clinicians, laboratory and rat handler teams, as well as all partners and donors who enabled the research into training and using TB detection rats. Main partners include the Sokoine University of Agriculture, the National Institute for Medical Research, MKUTA, and the National TB and Leprosy Programme in Tanzania; the University Eduardo Mondlane, the National Institute of Health, Associação Kenguelekezé, the Maputo City Health Authorities and the National TB Program in Mozambique; the Armauer Hansen Research Institute, the German Leprosy and TB Relief Association, the Prison Health Authorities and the National TB Program of Ethiopia; as well as the Technical University Braunschweig and Max Planck Institute for Infection Biology in Germany, and the University of Antwerp, Belgium.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.022 |
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".