Diagnostic accuracy of refractometry for assessing bovine colostrum quality: A systematic review and meta-analysis
Bibliographic record
Abstract
Provision of good quality colostrum [i.e., immunoglobulin G (IgG) concentration ≥50g/L] is the first step toward ensuring proper passive transfer of immunity for young calves. Precise quantification of colostrum IgG levels cannot be easily performed on the farm. Assessment of the refractive index using a Brix scale with a refractometer has been described as being highly correlated with IgG concentration in colostrum. The aim of this study was to perform a systematic review of the diagnostic accuracy of Brix refractometry to diagnose good quality colostrum. From 101 references initially obtain ed, 11 were included in the systematic review meta-analysis representing 4,251 colostrum samples. The prevalence of good colostrum samples with IgG ≥50g/L varied from 67.3 to 92.3% (median 77.9%). Specific estimates of accuracy [sensitivity (Se) and specificity (Sp)] were obtained for different reported cut-points using a hierarchical summary receiver operating characteristic curve model. For the cut-point of 22% (n=8 studies), Se=80.2% (95% CI: 71.1-87.0%) and Sp=82.6% (71.4-90.0%). Decreasing the cut-point to 18% increased Se [96.1% (91.8-98.2%)] and decreased Sp [54.5% (26.9-79.6%)]. Modeling the effect of these Brix accuracy estimates using a stochastic simulation and Bayes theorem showed that a positive result with the 22% Brix cut-point can be used to diagnose good quality colostrum (posttest probability of a good colostrum: 94.3% (90.7-96.9%). The posttest probability of good colostrum with a Brix value <18% was only 22.7% (12.3-39.2%). Based on this study, the 2 cut-points could be alternatively used to select good quality colostrum (sample with Brix ≥22%) or to discard poor quality colostrum (sample with Brix <18%). When sample results are between these 2 values, colostrum supplementation should be considered.
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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 | 0.018 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".