Systematic Review and Meta-Analysis of Diagnostic Accuracy of Serum Refractometry and Brix Refractometry for the Diagnosis of Inadequate Transfer of Passive Immunity in Calves
Bibliographic record
Abstract
BACKGROUND: Transfer of passive immunity in calves can be assessed by direct measurement of immunoglobulin G (IgG) by methods such as radial immunodiffusion (RID) or turbidimetric immunoassay (TIA). IgG can also be measured indirectly by methods such as serum refractometry (REF) or Brix refractometry (BRIX). OBJECTIVES: To determine the accuracy of REF and BRIX for assessment of inadequate transfer of passive immunity (ITPI) in calves. DESIGN: Systematic review and meta-analysis of diagnostic accuracy studies. METHODS: Databases (PubMed and CAB Abstract, Searchable Proceedings of Animal Science) and Google Scholar were searched for relevant studies. Studies were eligible if the accuracy (sensitivity and specificity) of REF or BRIX was determined using direct measurement of IgG by RID or turbidimetry as the reference standard. The study population included calves <14 days old that were fed with natural colostrum (colostrum replacement products were excluded). Quality assessment was performed by the QUADAS-2 tool. Hierarchical models were used for meta-analysis. RESULTS: From 1,291 references identified, 13 studies of 3,788 calves were included. Of these, 11 studies evaluated REF and 5 studies evaluated BRIX. The median (range) prevalence of ITPI (defined as calves with IgG <10 g/L by RID or TIA) was 21% (1.3-56%). Risk of bias and applicability concerns were generally low or unclear. For REF, summary estimates were obtained for 2 different cutoffs: 5.2 g/dL (6 studies) and 5.5 g/dL (5 studies). For the 5.2 g/dL cutoff, the summary sensitivity (95% CI) and specificity (95% CI) were 76.1% (63.8-85.2%) and 89.3% (82.3-93.7%), and 88.2% (80.2-93.3%) and 77.9% (74.5-81.0%) for the 5.5 g/dL cutoff. Due to the low number of studies using the same cutoffs, summary estimates could not be obtained for BRIX. CONCLUSIONS AND CLINICAL IMPORTANCE: Despite their widespread use on dairy farms, evidence about the optimal strategy for using refractometry, including the optimal cutoff, are sparse (especially for BRIX). When using REF to rule out ITPI in herds, the 5.5 g/dL cutoff may be used whereas for ruling in ITPI, the 5.2 g/dL cutoff may be used.
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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 | 0.019 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.038 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".