Investigation of blood biomarkers for the diagnosis of mild to moderate asthma in horses
Bibliographic record
Abstract
BACKGROUND: Asthma in horses is associated with nonspecific respiratory clinical signs and may be manifested only as exercise intolerance. Its diagnosis relies on bronchoalveolar lavage fluid (BALF) cytology in the presence of compatible clinical signs. The identification of blood biomarkers for this condition would facilitate diagnosis in the field, because there are regional areas where BAL is not routinely performed in clinical practice. OBJECTIVE: Identification of blood biomarkers for the diagnosis of asthma in horses. ANIMALS: Fourteen horses with asthma with increased neutrophil numbers in BALF (neutrophilic asthma), 9 healthy control horses, and 10 horses with other pathologic conditions (pathologic controls). METHODS: Physical examination, clinical score, hematology, and BALF cytology (in a subset of horses) were performed. Serum concentrations of surfactant protein D (SP-D), haptoglobin, and secretoglobin (SCGB) were measured using commercial ELISA assays. RESULTS: Serum concentration of SP-D > 43 ng/mL, serum concentration of haptoglobin >5730 ng/mL, and serum concentration of SCGB <19 ng/mL allowed differentiation of horses with neutrophilic asthma from horses of the control groups (healthy and pathologic) with sensitivity of 55, 95, and 75%, and specificity of 67, 28, and 60%, respectively. Specificity of 100% and sensitivity of 45% were obtained with the combination of SP-D, haptoglobin, and SCGB at the serum concentrations indicated above. Specificity of 95% and sensitivity of 45% were obtained with the combination of SP-D and SCGB serum concentrations. CONCLUSIONS AND CLINICAL IMPORTANCE: Haptoglobin, SCGB, and SP-D may be diagnostic aids in horses with clinical signs of lower airway disease and neutrophilic pulmonary inflammation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".