Pouched rats as detectors of tuberculosis: comparison to concentrated smear microscopy
Bibliographic record
Abstract
In 2014, 1.5 million people died of tuberculosis (TB), a disease that can be cured in nearly every case if detected in time. Rapid and accurate detection of TB is a crucial component of the World Health Organization's 2016–2035 End TB Strategy [1]. Pouched rats, Cricetomys ansorgei (previously called Cricetomys gambianus [2]), are able to detect Mycobacterium tuberculosis by sniffing sputum samples [3]. Since 2007, they have been used for second-line screening of sputum samples previously evaluated by Ziehl–Neelsen microscopy (ZN) at clinics in Tanzania. Use of the rats increases new case detections by ∼40% [3]. Pouched rats find 60% of TB patients that are missed by clinics but identifiable with concentrated smear microscopy We would like to thank the Mozambican National Tuberculosis Programme, the National Institute of Health and the Maputo City Health Directorate for facilitating access to public clinics in Maputo (Mozambique).
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".