Associations Between Pathogen-Specific Cases of Clinical Mastitis and Somatic Cell Count Patterns
Bibliographic record
Abstract
Associations were estimated between pathogen-specific cases of clinical mastitis (CM) and somatic cell count (SCC) patterns based on deviations from the typical curve for SCC during lactation and compared with associations between pathogen-specific CM and lactation average SCC. Data from 274 Dutch herds recording CM over an 18-mo period were used. Pathogens found were Staphylococcus aureus, coagulase-negative staphylococci, Escherichia coli, Streptococcus dysgalactiae, Streptococcus uberis, streptococci other than Strep. dysgalactiae and Strep. uberis, and culture-negative samples. The dataset contained 245,595 test-day records on SCC, recorded in 24,012 lactations of 19,733 cows of different parities. Pattern definitions were based on three or five consecutive test-day records. The patterns differentiated between a short or longer period of increased SCC and also between lactations with and without recovery. Logistic regression was applied to identify associations between presence of patterns and occurrence of pathogens. Occurrence of overall CM in a lactation is equally or even more accurately predicted by the presence of SCC in that lactation, than by a lactation average SCC of more than 200,000 cells/mL. Patterns can also distinguish between chances of risk for specific mastitis-causing pathogens. Clinical E. coli mastitis was significantly associated with the presence of a short peak in SCC, whereas Staph. aureus was associated with long increased SCC. Streptococcus dysgalactiae was not strongly associated with any of the defined patterns of peaks in SCC, and no single unambiguous pattern was found for Strep. uberis.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".