Development of a predictive model for bovine leukemia virus proviral load
Bibliographic record
Abstract
Abstract Background There is currently no commercially available method in Canada to identify bovine leukemia virus (BLV)-positive cows with high proviral load (PVL). Objectives First, develop a model to predict PVL using common, commercially available, cost-effective diagnostic tests. Second, investigate the relationship between lymphocyte count and PVL in BLV-positive cows. Animals A total of 339 BLV-positive and 62 BLV-seronegative cows on 15 dairy farms. Methods Cross-sectional study. Blood and milk samples were collected from all lactating BLV-positive cows on each farm and 5 to 10 BLV-seronegative cows depending on herd size. Blood and milk samples were tested for anti-BLV antibodies using enzyme-linked immunosorbent assay (ELISA). Complete blood counts were performed on blood samples, and standard components analyses were obtained for milk samples. Proviral load was determined by quantitative polymerase chain reaction for each cow. Results The inverse of lymphocyte count, the square of the inverse of lymphocyte count, and milk ELISA percent positivity were positively associated with increasing PVL in BLV-positive cows. For BLV-positive cows, lymphocyte count >5.2 × 109/L predicted a high PVL (BLV:Bovine DNA of >1 in blood) with a sensitivity of 92.4% and a specificity of 79.8%. For BLV-positive cows, white blood cell count >10.8 × 109/L predicted a high PVL, with a sensitivity of 85.5% and a specificity of 83.6%. Conclusions and Clinical Importance Based on these results, producers can implement commonly available diagnostic tests to identify cows with high probability of having high PVL, which may help in designing effective disease control strategies for BLV-positive herds.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".