Risk indicators associated with Staphylococcus aureus subclinical mastitis in smallholder dairy cows in Tanzania
Bibliographic record
Abstract
A cross sectional study was carried out between June and September 2003 to establish the prevalence and related risks indicators for Staphylococcus aureus (S. aureus) subclinical mastitis on smallholder dairy herds in Dar es Salaam region, Tanzania. 191 lactating cows from 64 randomly selected herds were investigated. However, due to such reasons as aggressive cow, blind quarters and presence of open teat wound, the California mastitis test (CMT) was carried out on 726 (9 cows and 2 quarters were not sampled) quarter milk samples. Herd (at least one positive quarter per cow), cow and quarter level prevalence of subclinical mastitis defined by CMT ≥ + were 100 %, 91.2 % and 85.0 % respectively. S. aureus was isolated in 21.0 % of the 718 (11 cows and 2 quarters were not sampled) - bacteriologically examined quarter milk samples. The cow and quarter level prevalence of sub-clinical mastitis, defined by positive CMT (≥ +) score and S. aureus positive culture, was 97.4 % and 98.0 %, respectively. Water availability, residual suckling, single uddertowel, poor housing sanitation, teat-lesions and the failure to use dry-cow therapy were the most substantial (ρ < 0.05) risk indicators. It is concluded that intensive and continuous farmer education programmes particularly focusing on subclinical mastitis prevalence and ways to reduce or eliminate these infections, are necessary to improve udder health in Tanzania. These issues are discussed at the end of the paper.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".