Usefulness of differential somatic cell count for udder health monitoring: Diagnostic performance of somatic cell count and differential somatic cell count for diagnosing intramammary infections in dairy herds with automated milking systems
Bibliographic record
Abstract
Mastitis poses significant economic challenges for dairy farms. Therefore, enhancing the accuracy of diagnostic methods for detecting IMI can potentially improve prevention, control, and treatment strategies. The SCC is a well-established parameter for identifying inflammation resulting from IMI. Given the recent introduction of differential somatic cell count (DSCC) for routine milk sample screening, limited research has been conducted to assess its additional benefits for diagnosing IMI. Therefore, our main objective was to evaluate the diagnostic accuracy of SCC, DSCC, and SCC-DSCC combinations in detecting IMI caused by any pathogen or by major pathogens using quarter milk samples. Five dairy herds using automated milking systems were selected using convenience sampling in Québec, Canada. Determination of SCC and DSCC was performed by Lactanet (Ste-Anne de Bellevue, QC, Canada) using a CombiFoss 7 DC instrument. A 5-populations 2-tests Bayesian latent class models was used, with bacteriological culture employed as the imperfect reference test. Posterior estimates for sensitivity (Se), specificity (Sp), and the predictive values for 2 hypotheticals IMI prevalences due to any pathogen or major pathogens were computed. The proportion of quarters positive for any pathogen or major pathogen using milk culture was 31.7% (5,125/16,176) and 5.4% (871/16,176), respectively. For the detection of IMI by any pathogen, using a serial interpretation for the combination of SCC ≥100,000 and DSCC at ≥65% increased the Sp from 0.71 (95% Bayesian credible intervals [95BCI]: 0.70, 0.72) to 0.84 (95BCI: 0.83, 0.86) compared with SCC alone at the cutoff ≥100,000 cells/mL, although resulting in a slight decrease in Se from 0.49 (95BCI: 0.43, 0.54) to 0.46 (95BCI: 0.42, 0.50). Moreover, for detecting IMI caused by major pathogens, combining SCC at the threshold of ≥100,000 cells/mL and DSCC at the threshold of ≥65% using serial interpretation increased the Sp from 0.68 (95BCI: 0.67, 0.69) to 0.80 (95BCI: 0.79, 0.81) compared with SCC alone at the ≥100,000 cells/mL threshold. Our findings suggest that DSCC could be combined with SCC to provide a modest improvement in Sp with minimal compromise in Se for identifying IMI caused by any or by major pathogens. In addition, DSCC combined with SCC provided a small improvement in Se for detecting any pathogen using the parallel interpretation. However, no improvements in Se were observed when using the combination of SCC and DSCC for detecting major pathogens.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".