Ventilator-Assisted Inspiratory and Expiratory Breath-Hold Thoracic Computed Tomographic Scans Can Detect Dynamic and Static Airway Collapse in Dogs with Limited Agreement with Tracheobronchoscopy
Bibliographic record
Abstract
Airway collapse (AC) in dogs includes a tracheal collapse, mainstem and lobar bronchial collapse, and bronchomalacia (i.e., segmental/subsegmental bronchial collapse). The clinical presentation of AC may overlap with non-collapsible airway disease (NCAD) or another non-lower airway respiratory disease (NLARD). This study determined whether paired inspiratory (I)/expiratory (E)-breath-hold computed tomography (I/E-BH CT) can detect a static and dynamic AC in dogs with spontaneous respiratory disease and it compared the CT-derived metrics of the AC to the tracheobronchoscopy metrics. The CT-acquired I and E diameter and cross-sectional area (CSA) for the trachea, mainstem and lobar bronchi in dogs with an AC (n = 16), NCAD (16), and NLARD (19) served for a dynamic percent of the airway narrowing (%AN) calculation. A scoring system assessed the bronchomalacia. The circularity was calculated for each airway. The results were compared to the tracheobronchoscopy collapse grading. In the dogs with an AC, the %AN was larger for the trachea, right mainstem bronchus and right middle lobar bronchus when they were compared to the dogs with NCAD and NLARD. Flattening was only identified for the trachea of the AC dogs. The agreement between the CT and tracheobronchoscopy scores was 20% from trachea to the lobar bronchi and 47% for the segmental/subsegmental bronchi. Paired I/E-BH CT can detect static and dynamic AC with limited agreement with the tracheobronchoscopy metrics. Independent scoring systems that are tailored to the clinical manifestations of functional impairments are needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".