Computed tomographic assessment of principal bronchial anatomy in dogs of various thoracic conformations: 93 cases (2012–2017)
Bibliographic record
Abstract
OBJECTIVE: To better understand spatial relationships between principal bronchi and other intrathoracic structures by use of CT images of dogs of various somatotypes. ANIMALS: 93 dogs that underwent thoracic CT. PROCEDURES: Information was collected from medical records regarding signalment and physical examination and echocardiographic findings. Two investigators recorded multiple measurements on a thoracic axial CT image from each dog. RESULTS: Thoracic height-to-width ratio (H:W) was associated with left principal bronchus (LPB) and right principal bronchus (RPB) H:W, aortic-LPB separation, focal LPB narrowing, and aortic-vertebral overlap. Thoracic H:W was not associated with dog age, weight, sex, or brachycephalic breed. Twenty-five (27%) dogs had focal LPB narrowing, compared with 5 (5%) dogs with focal RPB narrowing (P < 0.001). Ten of 25 dogs had overlap or contact between vertebrae, aorta, LPB, and heart, suggesting a cumulative compressive effect on the LPB, while 15 had LPB-aorta contact and lack of contact between the aorta and thoracic vertebrae, suggesting an aortic constrictive effect on the LPB. None had LPB narrowing without contact from surrounding structures. Inter-rater agreement was high. CLINICAL RELEVANCE: In dogs that underwent CT and were not selected for clinical suspicion of bronchial disease, principal bronchial morphology was associated with thoracic conformation. Focal LPB narrowing occurred more often than RPB narrowing. Focal LPB narrowing occurred with evidence of extraluminal compression, with or without contact between aorta and vertebrae. Brachycephalic breed could not be used for predicting thoracic H:W.
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 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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".