COMPARISON OF THORACIC ULTRASONOGRAPHY AND RADIOGRAPHY FOR THE DETECTION OF INDUCED SMALL VOLUME PNEUMOTHORAX IN THE HORSE
Bibliographic record
Abstract
Small volume pneumothorax can be challenging to diagnose in horses. The current standard method for diagnosis is standing thoracic radiography. We hypothesized that thoracic ultrasonography would be more sensitive. Objectives of this prospective, experimental study were to describe a thoracic ultrasound method for detection of small volume pneumothorax in horses and to compare results of radiography and ultrasound in a sample of horses with induced small volume pneumothorax. Six mature healthy horses were recruited for this study. For each horse, five 50 ml air boluses were sequentially introduced via a teat cannula into the pleural space. Lateral thoracic radiographs and standardized ultrasound (2D and M-mode) examinations of both hemithoraces were performed following administration of each 50 ml air bolus. Radiographs and ultrasound images/videos were analyzed for detection of pneumothorax by four independent investigators who were unaware of treatment status. Sensitivity, specificity, positive predictive values, negative predictive values, and agreement among investigators (Kappa test, κ) were calculated for radiography, 2D and M-mode ultrasound. Comparisons were made using a chi-squared exact test with significance set at P < 0.05. Two-dimensional (84%) and M-mode (80%) ultrasound were more sensitive than radiography (48%) for pneumothorax detection (P = 0.02 and P = 0.04, respectively). Specificity and positive predictive values were similar for all three imaging modalities (P = 1). Agreement between investigators for pneumothorax detection was excellent for 2D ultrasound (κ = 1), very good for M-mode ultrasound (κ = 0.87), and good for radiography (κ = 0.79). Findings from this experimental study supported the use of thoracic ultrasonography as a diagnostic method for detecting pneumothorax in horses.
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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 | 0.001 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".