Evaluation of a risk‐screening questionnaire to detect equine lung inflammation: Results of a large field study
Why this work is in the frame
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Bibliographic record
Abstract
REASONS FOR PERFORMING STUDY: The diagnosis of equine recurrent airway obstruction (RAO) and inflammatory airway disease (IAD) is based on clinical signs and increased inflammatory cell percentages in the bronchoalveolar lavage (BAL) fluid. Since a BAL is an invasive procedure, a risk-screening questionnaire (RSQ) would be a valuable screening tool for lung inflammation. OBJECTIVE: To evaluate the accuracy of a RSQ to detect lower airway inflammation (LAI) in a large population of horses. METHODS: A standardised BAL was performed in the field on 167 horses in Alberta, Canada. Horses were separated into 3 categories: 1) BAL normal; 2) BAL mild to moderate LAI (MLAI), and 3) BAL severe LAI (SLAI). The horse owners were asked to complete a RSQ. The RSQ scores were compared to the BAL results to determine the likelihood of a horse having MLAI, SLAI or no LAI. RESULTS: Based on BAL cytology, 28 (17%) horses were normal and 139 (83%) were abnormal, with 110 (66%) showing MLAI and 29 (17%) SLAI. Horses with SLAI and MLAI had a mean RSQ score of 0.95 and 0.70, respectively, compared to 0.60 for normal BAL horses. Horses with SLAI showed more clinical signs than normal and MLAI horses. The sensitivity and negative predictive values of the RSQ for detecting SLAI using a cut-off score of 0.87, were excellent at 0.90 (95%CI 0.73-0.98) and 0.96 (95%CI 0.82-1.00). Questions on the clinical signs typically found in RAO cases differed significantly between horses with BAL SLAI and those with BAL normal. CONCLUSIONS: Prevalence of MLAI was high in this population. Although the RSQ did not allow differentiating normal horses from horses with MLAI, it has a high sensitivity to detect horses with SLAI and is therefore a good screening tool for SLAI.
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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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 it