Use of Fourier-Transform Infrared Spectroscopy for the Diagnosis of Failure of Transfer of Passive Immunity and Measurement of Immunoglobulin Concentrations in Horses
Bibliographic record
Abstract
Background: The economic, accurate, and rapid screening of foals for failure of transfer of passive immunity (FPT) is essential to ensure timely intervention. Hypothesis: Infrared (IR) spectroscopy of foal sera and pattern recognition may be used to diagnose FPT and quantify serum IgG. Samples: Sera from 194 foals (24–72 hours) with serum immunoglobulin G (IgG) concentrations determined previously by radial immunodiffusion assay (RID) were used. Methods: IR spectra were recorded for the serum samples, and the data were randomly divided into training and independent test sets, each containing both FPT-positive (IgG <400 mg/dL) and non-FPT samples. A genetic optimal region selection algorithm and linear discriminant analysis were used to partition the training spectra, and the resulting classifier was then validated by comparing the IR-predicted FPT status for each of the test samples to that provided by the RID IgG assay. A quantitative IR-based assay for IgG was developed using partial least squares (PLS) and validated by testing its ability to predict IgG concentrations. Results: Specificity, sensitivity, and accuracy for the combined data were 92.5, 96.8, and 95.9%, respectively. Corresponding positive (88.1%) and negative predictive (98.0%) values determined a success rate of 95–97% as compared to RID-based IgG concentrations. The IR-based quantitative assay yielded correlation coefficients for IR spectroscopy versus RID-based IgG concentrations of 0.90 and 0.86 for the training and test sets, respectively. Conclusions and Clinical Importance: The overall performance of the IR-based test was similar to that of the colorimetric assay and was superior and more economic than other available tests.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".