Evaluation of 3 refractometers to determine transfer of passive immunity in neonatal beef calves
Bibliographic record
Abstract
BACKGROUND: Refractometry is used to assess transfer of passive immunity (TPI), but studies evaluating different refractometers and appropriate thresholds for recommended target immunoglobulin G (IgG) concentrations for beef calves are limited. OBJECTIVES: To evaluate test performance of digital (DSTP) and optical (OSTP) serum total protein (STP) refractometers and a digital Brix (DBRIX) refractometer for assessment of passive immunity in beef calves. ANIMALS: A total of 398 beef calves from 6 herds, 1 to 7 days of age. METHODS: Serum IgG concentration was estimated by DSTP, OSTP, and DBRIX, and measured by radial immunodiffusion (RID). Correlation coefficients (r) among results were calculated. Optimal STP and Brix thresholds for identification of IgG <10, <16, and <24 g/L were determined using interval likelihood ratios. Refractometer performance and agreement were assessed using areas under the curve (AUC), diagnostic test characteristics, Cohen's kappa (κ), and Bland-Altman analysis. RESULTS: Refractometer results were highly correlated with RID (r = 0.82-0.91) and with each other (r = 0.91-0.95), and overall test performance was excellent (AUC = 0.93-0.99). The STP concentrations of ≤5.1, ≤5.1, and ≤5.7 g/dL and Brix percentages of ≤7.9%, ≤8.3%, and ≤8.7% indicated IgG concentrations <10, <16, and <24 g/L, respectively. Agreement of refractometers with RID was variable (κ = 0.46-0.80) and among refractometers was substantial (κ = 0.62-0.89). CONCLUSIONS AND CLINICAL IMPORTANCE: All refractometers showed good utility as monitoring tools for assessment of TPI in beef calves.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".