Risk Factors and Outcomes in Dogs With Respiratory Disease Undergoing Diagnostic Airway Lavage
Bibliographic record
Abstract
Advanced diagnostic testing is becoming increasingly important to accurately assess pulmonary parenchymal, airway, and pulmonary vascular diseases in dogs. Due to respiratory system compromise, diagnostic procedures performed under general anesthesia, including thoracic computed tomography (CT) and bronchoalveolar lavage (BAL), are thought to carry significant risk to dogs with respiratory disease. In lieu of performing these diagnostics, empirical medical therapy is often administered, potentially delaying appropriate therapy or providing unnecessary treatment. This study prospectively evaluated risk factors and outcomes for dogs with respiratory disease undergoing general anesthesia for thoracic CT and BAL. Arterial blood gas samples were taken pre- and post-BAL to evaluate pulmonary gas exchange. Pre-BAL arterial partial pressure of oxygen-to-fractional inspired oxygen ratio was used to stratify dogs into groups of mild or moderate to severe disease severity. A novel thoracic CT disease severity scoring system was used to independently stratify dogs into mild or moderate to severe groups. Statistical comparisons between groups were made for signalment, body weight, temperature, pulse, respiratory rate, WBC count, ventilator-acquired pulmonary mechanics (specific compliance and resistance), change in arterial partial pressure of oxygen post-BAL, and outcomes. Seventeen dogs were prospectively enrolled. A comparatively lower heart rate at presentation was the only potential marker of increased disease severity identified when stratified by CT severity score. Arterial partial pressure of oxygen did not significantly decrease post-BAL regardless of disease severity or stratification method. The CT scoring system significantly correlated with the pre-BAL arterial partial pressure of oxygen-to-fractional inspired oxygen ratio. Incidence of post-procedural complications was 18%, with all complications being transient. Mortality as a direct complication of diagnostics was 0%. When considering euthanasia secondary to severity of the underlying disease and poor prognosis or death due to unrelated disease, mortality was 18%. In dogs with respiratory disease undergoing advanced diagnostic procedures, the overall incidence of post-procedural morbidity was low with no mortality directly attributed to the procedures. A novel CT disease severity scoring system was utilized and shows promise as a tool for evaluation of disease severity in this patient population when compared to arterial blood gas analysis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".