Diagnostic accuracy of Somaticell, California Mastitis Test, and microbiological examination of composite milk to detect Streptococcus agalactiae intramammary infections
Bibliographic record
Abstract
The objectives of this study were to estimate the accuracy of Somaticell (Idexx Laboratories Inc., Westbrook, ME), California Mastitis Test (CMT), and microbiological examination of composite milk (MEC) to diagnose Streptococcus agalactiae intramammary infections (IMI), and to assess the agreement between Somaticell and CMT to detect these infections. A secondary objective was to estimate quarter- and cow-level prevalence of S. agalactiae IMI in the herds included in the study. Seven farms were included in the study. The CMT was performed and aseptic milk samples were collected from all quarters of all lactating cows. Composite milk samples were produced in the laboratory by mixing milk from all quarters of each sampled cow. The Somaticell test was performed on a subset of S. agalactiae-positive (n = 167) and S. agalactiae-negative (n = 152) quarter milk samples. Microbiological examination of quarter milk samples (MEQ) was considered the reference test for diagnosing S. agalactiae IMI. The accuracy of all tests at various thresholds was estimated using Bayesian latent class models. Apparent prevalence of S. agalactiae IMI was 15.8% (n = 184/1,164) at the quarter level (based on MEQ) and 28.5% (n = 83/291) at the cow level (based on MEC). True prevalence, as determined by Bayesian models, was 13.0% [95% credible interval (CR): 6.4-24.4%] at the quarter level, and 25.6% (95% CR: 15.3-39.5%) at the cow level. At the cow level (n = 285), sensitivity and specificity of MEC were 95.6 and 99.5%, respectively. The accuracy of Somaticell (n = 319 quarters) to identify S. agalactiae-infected quarters was 75.4, 86.4, 88.9, 89.4, and 91.0% at thresholds of 98,000, 147,000, 205,000, 244,000, and 282,000 cells/mL, respectively. The accuracy of CMT was 87.6, 90.7, 90.8, and 87.4% at thresholds of trace, 1, 2, and 3, respectively. The areas under the receiver operating characteristic curve for Somaticell and CMT were 94.5% (95% confidence interval: 91.8-97.2%) and 92.0% (88.6-95.4%), respectively. At the tested thresholds, the sensitivity of Somaticell ranged from 94.9 to 99.5% to detect S. agalactiae IMI, and specificity ranged from 48.1 to 87.1%. The sensitivity of Somaticell at the lowest threshold (69,000 cells/mL; sensitivity = 99.9%; 95% CR: 98.2-100%) was higher than that of CMT at any tested threshold. Results of this study could be used at the farm level to reduce the use of antimicrobials and reach specific goals in S. agalactiae eradication programs.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".