Diagnosis of Cattle Diseases Endemic to Sub-Saharan Africa: Evaluating a Low Cost Decision Support Tool in Use by Veterinary Personnel
Bibliographic record
Abstract
BACKGROUND: Diagnosis is key to control and prevention of livestock diseases. In areas of sub-Saharan Africa where private practitioners rarely replace Government veterinary services reduced in effectiveness by structural adjustment programmes, those who remain lack resources for diagnosis and might benefit from decision support. METHODOLOGY/PRINCIPAL FINDINGS: We evaluated whether a low-cost diagnostic decision support tool would lead to changes in clinical diagnostic practice by fifteen veterinary and animal health officers undertaking primary animal healthcare in Uganda. The eight diseases covered by the tool included 98% of all bovine diagnoses made before or after its introduction. It may therefore inform proportional morbidity in the area; breed, age and geographic location effects were consistent with current epidemiological understanding. Trypanosomosis, theileriosis, anaplasmosis, and parasitic gastroenteritis were the most common conditions among 713 bovine clinical cases diagnosed prior to introduction of the tool. Thereafter, in 747 bovine clinical cases estimated proportional morbidity of fasciolosis doubled, while theileriosis and parasitic gastroenteritis were diagnosed less commonly and the average number of clinical signs increased from 3.5 to 4.9 per case, with 28% of cases reporting six or more signs compared to 3% beforehand. Anaemia/pallor, weakness and staring coat contributed most to this increase, approximately doubling in number and were recorded in over half of all cases. Finally, although lack of a gold standard hindered objective assessment of whether the tool improved the reliability of diagnosis, informative concordance and misclassification matrices yielded useful insights into its role in the diagnostic process. CONCLUSIONS/SIGNIFICANCE: The diagnostic decision support tool covered the majority of diagnoses made before or after its introduction, leading to a significant increase in the number of clinical signs recorded, suggesting this as a key beneficial consequence of its use. It may also inform approximate proportional morbidity and represent a useful epidemiological tool in poorly resourced areas.
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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 | 0.017 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".