Optimal cutoff estimation and evaluation of direct and indirect diagnostic methods for assessing bovine colostrum quality with Bayesian finite mixture models
Bibliographic record
Abstract
Ensuring high-quality colostrum for newborn calves is essential for their health and future productivity. We applied Bayesian finite mixture models to estimate optimal cutoff values and evaluate the diagnostic accuracy of 3 methods-radial immunodiffusion (RID) assay, transmission infrared (TIR) spectroscopy, and digital Brix (dBrix) refractometry-measured on a continuous scale for assessing bovine colostrum quality, using 591 colostrum samples from 42 Holstein dairy herds in Atlantic Canada. The mean and standard deviation of IgG concentrations for high-quality colostrum were 61.07 ± 39.8 g/L, 51.28 ± 27.38 g/L, and 24.32 ± 4.13% Brix for RID assay, TIR spectroscopy, and dBrix refractometry, respectively, compared with 19.93 ± 15.54 g/L, 7.78 ± 37.4 g/L, and 15.87 ± 3.45% Brix for low-quality samples. The prevalence of high-quality colostrum was estimated at 83% (95% credible interval [CrI]: 0.79-0.88). The dBrix refractometer demonstrated the highest discriminatory power, with an area under the curve (AUC) of 0.94 (95% CrI: 0.91-0.97), followed by RID assay (AUC: 0.92; 95% CrI: 0.88-0.96) and TIR spectroscopy (AUC: 0.82; 95% CrI: 0.76-0.88). Optimal cutoff values were determined using Youden's index: 34.15 g/L for RID assay (sensitivity [Se] = 0.86, specificity [Sp] = 0.83), 22.74 g/L for TIR spectroscopy (Se = 0.88, Sp = 0.66), and 19.62% Brix for dBrix refractometry (Se = 0.90, Sp = 0.85). Correlation between RID assay and TIR spectroscopy was stronger for high-quality colostrum samples (0.80; 95% CrI: 0.77-0.84) than for low-quality samples (0.36; 95% CrI: 0.16-0.55), indicating that these methods are not perfectly correlated and justifying the need for multiple diagnostic approaches. Among individual methods, dBrix refractometry showed the highest positive predictive value (PPV = 1.00), and all methods demonstrated moderate negative predictive values (NPV = 0.46-0.57). Combining methods in series interpretation increased PPV up to 1.00 when all 3 methods were used together, though with reduced NPV. Conversely, parallel interpretation substantially improved NPV, reaching 0.98 when all 3 methods were combined. By modeling continuous measurements instead of dichotomized test results, our analysis produced refined cutoff values for assessing colostrum quality. The findings indicate that existing thresholds remain largely adequate, offering only minor performance improvements, and emphasize the need to balance diagnostic refinements with their potential effects on calf management and passive immunity. Furthermore, our findings suggest that, although individual assessment methods offer valuable diagnostic information, combining multiple methods can optimize either Se or Sp, depending on the interpretation approach, thereby further enhancing the accuracy of colostrum quality evaluation.
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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 | 0.041 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".