Development and validation of BLRI Mastitis Test Kit at Bangladesh Livestock Research Institute Regional Station, Sirajganj
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to develop a low-cost kit for the detection of subclinical mastitis (SCM) and to check its validity, reproducibility, and efficacy at the field level. MATERIALS AND METHODS: A total of 550 quarter milk samples from crossbred dairy cows were collected, of which 400 milk samples were used to validate the newly developed BLRI mastitis test (BMT) kit to justify its efficacy as an individual test kit in detecting SCM based on somatic cell count (SCC) by direct microscopic count (DMC). The efficacy of the newly developed BMT was compared with the California Mastitis Test (CMT) kit. Another 150 milk samples were subjected to SCC determined by DMC and DCC (De Laval cell counter®) categorized by CMT and BMT scores. RESULTS: A SCM test kit, namely, BMT kit was successfully developed in this study. The percentage accuracy of CMT and BMT were 76.75% and 75.75%; sensitivity 69.36% and 67.56%; specificity 85.95% and 85.85%; positive predictive value 86.03% and 85.71%; negative predictive value 69.23% and 68%, respectively. A p value of 0.001 was found for both CMT and BMT. However, CMT and BMT had no significant difference in sensitivity (p = 0.778). Average SCCs (cells/ml) determined by DCC and DMC, respectively, were mostly corresponded to the SCC ranges of both CMT and BMT scores. CONCLUSION: The newly developed BMT kit is an independent, cheap, farmer-friendly, first country made, and reliable SCM diagnostic test kit that can be used at field condition.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".