Evaluation of an automated on-farm device to identify groups of bacteria associated with clinical mastitis on a United States dairy farm
Bibliographic record
Abstract
Recent studies suggest that selectively treating non-severe clinical mastitis during lactation, guided by on-farm culture, can be prudent. However, on-farm culture presents logistical challenges. This study investigated Mastatest’s® (MT; Mastaplex, New Zealand) ability to identify broad groups of pathogens in clinical mastitis milk samples compared to laboratory culture and matrix-assisted laser desorption ionization-time of flight mass spectrometry (LC+M) on one U.S. dairy farm. Milk samples from quarters with clinical mastitis were collected on a dairy farm located in Colorado (n = 162). The test’s ability to identify groups of pathogens was assessed against LC+M (reference method) using contingency tables. Test characteristics (sensitivity [Se], specificity [Sp], positive predictive value and negative predictive value) and their 95% confidence intervals were estimated. Agreement was assessed using the Kappa statistic. Prevalences (0.20-0.60) for Gram-positive and Gram-negative bacteria were used to estimate predictive values. Before analysis, contaminated or missing samples in either MT or LC+M were excluded (n = 36). Our findings indicated moderate to substantial agreement, with Kappa values ranging from 0.53 to 0.66. For Gram-positive pathogens, Se and Sp estimates, and their 95% confidence intervals were: Se: 0.83 (0.70, 0.92), Sp: 0.81 (0.70, 0.89), and for Gram-negative bacteria (Se: 0.73 [0.50, 0.89], Sp: 0.93 [0.87, 0.97]). Negative predictive value remained above 0.70 across different prevalence scenarios, particularly where values exceeded 0.90 for prevalences below 0.30. Our findings suggest MT is a suitable technology for on-farm detection of mastitis pathogens commonly identified on U.S. dairy farms in clinical mastitis milk samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".