Use of somatic cell counts and California mastitis test results from individual quarter milk samples to detect subclinical intramammary infection in dairy cattle from a herd with a high bulk tank somatic cell count
Bibliographic record
Abstract
OBJECTIVE: To determine whether somatic cell counts (SCCs) or California mastitis test (CMT) scores for individual quarter milk samples could be used to detect subclinical intramammary infection among dairy cattle in a herd with a high bulk tank SCC. DESIGN: Prospective clinical trial. ANIMALS: 278 Holstein-Friesian dairy cattle from a single herd. PROCEDURE: Individual quarter milk samples were collected and submitted for bacterial culture, California mastitis testing, and determination of SCC. Additional milk samples were collected 34 days later and submitted for bacterial culture. RESULTS: During the initial visit to the herd, milk samples were collected from all 278 cows. However, because of blind mammary quarters or missing data, results for 1,057 quarter milk samples were included. Bacterial culture did not yield any growth for 622 (58.8%) of these samples. Regardless of the cutoff that was used, sensitivity of the CMT score was < or = 0.50 and sensitivity of the SCC linear score (SCS) was < or = 0.60. For 497 mammary quarters, results of bacterial culture of samples collected 34 days apart were concordant; bacterial culture did not yield any growth for 342 (68.8%) of these quarters. Regardless of the cutoff that was used, sensitivity of the CMT score was < or = 0.61 and sensitivity of the SCS was < or = 0.76 for mammary quarters with concordant bacterial culture results. CONCLUSIONS AND CLINICAL RELEVANCE: Results suggest that neither CMT score nor SCC is sensitive enough to be useful as a screening test for identifying infected mammary quarters among dairy cattle in a herd with high bulk tank SCC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".