Short communication: Haptoglobin as an early indicator of metritis
Bibliographic record
Abstract
The purpose of this study was to determine whether haptoglobin (Hp) could be used as a predictive measure for metritis. Cattle were grouped into 3 health categories based on the condition of vaginal discharge and body temperature after calving: severe metritis (n = 12), mild metritis (n = 32), and healthy (n = 23). Blood was collected and analyzed for Hp concentration on d -20 +/- 5, -6 +/- 2, -2 +/- 1, and d 0 relative to calving, and then every 3 d after calving until d +21. Cows with mild and severe metritis had greater Hp concentrations than healthy cows between d 0 and d +12. Mean (+/-SE) Hp concentrations peaked on d +3 in the cows with mild metritis (1.06 +/- 0.15 g/L) and on d +6 in cows with severe metritis (1.62 +/- 0.47 g/L). Mean concentrations for the healthy group were 0.58 +/- 0.12 g/L and 0.31 +/- 0.08 g/L on d +3 and d +6, respectively. Clinical signs of pathological discharge for the mildly and severely metritic cows did not occur until, on average, 8.6 +/- 3.9 d and 5.3 +/- 1.9 d after calving, respectively. Cows with Hp concentrations >or=1 g/L on d +3 were 6.7 times more likely to develop severe or mild metritis; this predictive threshold has a sensitivity of 50% and specificity of 87%. These results indicate that an acute phase inflammatory response precedes clinical metritis and that Hp screening may assist in the early detection of metritis, providing increased opportunities for early treatment and prevention.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".