Diagnostic accuracy of refractometry methods for estimating passive immunity status in neonatal beef calves
Bibliographic record
Abstract
BACKGROUND: Assessing the inadequate transfer of passive immunity (ITPI) in beef calves is crucial because calves with ITPI are at high risk for morbidity and mortality. OBJECTIVES: The aim of this study was to determine the accuracy of digital Brix (D-BRIX) and digital serum total protein (D-STP) refractometers to estimate different passive immunity status in beef calves and to determine the robustness of thresholds. METHODS: Blood samples were collected from 202 (1-7 days old) beef calves. Serum total solid percentages, total protein concentrations, and IgG concentrations were measured with the D-BRIX refractometer, D-STP refractometer, and gold standard radial immunodiffusion (RID) assay, respectively. Data were analyzed using diagnostic test accuracy, areas under the receiver operating characteristics curve, Cohen's kappa coefficient, and misclassification costs analysis to estimate IgG concentrations <10, <16, and <24 mg/mL. RESULTS: For the prediction of serum IgG concentrations <10, <16 and <24 mg/mL, the optimal cut-off values were determined to be <8.5% (Se: 100.0% (95% CI: 87.9-100.0); Sp: 94.2% [95% CI: 89.6-97.2]), <8.5% (Se: 92.1% [95% CI: 78.6-98.2]; Sp: 97.6% [95% CI: 93.9-99.3]), and <10.1% (Se: 88.8% [95% CI: 79.7-94.7]; Sp: 67.2% [95% CI: 58.1-75.4]), respectively, for the D-BRIX refractometer; and <5.2 g/dL (Se: 100.0% [95% CI: 87.9-100.0]; Sp: 93.6% [95% CI: 88.9-96.8]), <5.2 g/dL (Se: 92.1% [95% CI: 78.6-98.2]; Sp: 97.0% [95% CI: 93.0-99.0]), and <6.4 g/dL (Se: 87.5% [95% CI: 78.2-93.8]; Sp: 69.7% [95% CI: 60.7-77.7]), respectively, for the D-STP refractometer. CONCLUSIONS: The digital Brix and digital serum total protein refractometers can be used as monitoring tools for assessing passive immunity transfer in neonatal beef calves.
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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".