Usefulness of digital and optical refractometers for the diagnosis of failure of transfer of passive immunity in neonatal foals
Bibliographic record
Abstract
BACKGROUND: Neonatal foals with failure of transfer of passive immunity (FTPI) are at higher risk of morbidity and mortality. Successful treatment of FTPI is time-dependent, thus rapid and accurate measurement of serum IgG concentration is important for the management and care of neonatal foals. OBJECTIVES: To validate the use of digital and optical refractometers for assessing FTPI in neonatal foals and compare the diagnostic performance and level of agreement of the two refractometers to the reference standard radial immunodiffusion (RID) assay. STUDY DESIGN: A retrospective validation study. METHODS: Serum samples (n = 253) were collected from 230 foals admitted to the Veterinary Teaching Hospital and Ambulatory Equine Service between 2012 and 2017. The serum IgG concentrations were measured by the reference RID assay, digital Brix and optical refractometers. The correlation between results of two refractometers and RID assay was assessed. A receiver operating characteristic curve was created and used to identify the optimal cut-offs for evaluating sensitivity and specificity of the two refractometers to detect foals with complete and partial FTPI. RESULTS: The RID-IgG concentrations were positively correlated with the Brix scores obtained from a digital refractometer (r = 0.73, P = 0.001) and serum total protein obtained from an optical refractometer (r = 0.72, P = 0.001). The sensitivity and specificity of the digital Brix refractometer at optimal cut-off (≤7.8% Brix) were 88.1 (95% CI: 74.4-96.0) and 67.7% (95% CI: 60.6-74.3) to detect RID-IgG<4 g/L and 79.0 (95% CI: 68.5-87.3) and 77.3% (95% CI: 69.8-83.8) to detect RID-IgG≤8 g/L, respectively. The sensitivity and specificity of the optical refractometer at optimal cut-off (≤42 g/L) were 86.1 (95% CI: 72.1-94.7) and 70.9% (95% CI: 63.9-77.3) to detect RID-IgG<4 g/L and at cut-off (≤44 g/L) were 82.9 (95% CI: 73.0-90.3) and 72.7% (95% CI: 64.8-79.6) to detect RID-IgG≤8 g/L, respectively. MAIN LIMITATIONS: The number of diseased foals was small to investigate the validity of the selected cut-off values for assessing FTPI in sick foals. CONCLUSIONS: The two refractometers exhibit utility as rapid, inexpensive screening tests and have a good sensitivity for assessing FTPI in neonatal foals.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".