Diagnosing intramammary infections: Evaluation of composite milk samples to detect intramammary infections
Bibliographic record
Abstract
Composite milk samples, in which milk from all 4 bovine quarters is collected in a single vial, are widely used in many developed dairy industries for detection of intramammary infections (IMI). These samples are more economical for use in culturing protocols than individual quarter samples, and may be useful when considering management options at the cow and herd level. The dilution effect may be problematic, however, resulting in lower sensitivity (Se) in IMI detection on composite samples. Relative Se and specificity (Sp) in composite samples have previously been described for some major pathogens, but because the causative organism for IMI is initially unknown, it is beneficial to investigate the reliability of composite samples for detection of all types of mastitis-causing bacteria. The Canadian Bovine Mastitis Research Network has a large data collection platform-the National Cohort of Dairy Farms-containing a vast amount of data on mastitis in Canada. These data have been used to further investigate the Se and Sp of composite samples in detecting IMI caused by specific mastitis pathogens. Milk samplings of selected cows before dry-off, after calving, and during lactation (n=48,835 samples) were employed to this end. Composite samples showed moderately high Se for Staphylococcus aureus (77.1%, 95% CI=73.3-80.5) and Streptococcus dysgalactiae (73.4%, 95% CI=60.9-83.7), with moderate Se for Streptococcus uberis (62.1%, 95% CI=49.3-73.8) and coagulase-negative staphylococci (59.8%, 95% CI=58.4-61.2). Sensitivities always increased as the number of affected quarters increased. Composite samples also showed high Sp (>97%) for most organisms. Factors such as lactation number and stage of lactation were evaluated for their influence on the Se and Sp of composite sampling, but were only found to be significant for coagulase-negative staphylococci. Predictive values using the herd prevalences found across Canada were calculated and can be useful in field scenarios when composite sampling is employed to assist mastitis management. When used to detect newly occurring IMI in pairs of samples taken before dry-off, post-calving, and also prior to and subsequent to the dry period, composite samples were shown to have lower Se but similar Sp for all pathogens investigated. Composite samples can be used to detect IMI and new IMI in dairy cows, but the Se and Sp of the procedure should be taken into account.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".