Use of CT to evaluate and compare intranasal features in brachycephalic and normocephalic dogs
Bibliographic record
Abstract
OBJECTIVES: To evaluate and compare nasal mucosal contact, septal deviation and caudal aberrant nasal turbinates in brachycephalic and normocephalic dogs using computed tomography. METHODS: Dogs without nasal disease and having undergone computed tomography scan of the head (plica alaris to the cribiform plate) were retrospectively selected and divided into brachycephalic and normocephalic groups. Eighteen brachycephalic and 32 normocephalic dogs were included. Anatomic criteria were used to locate predetermined pairs of intranasal structures and nasal mucosal contact was described as present or absent for each site. Septal deviations were identified and measured using angle of septal deviation. Caudal aberrant nasal turbinates were identified and categorised when present. RESULTS: Prevalence of nasal mucosal contact was significantly higher in brachycephalic dogs. No significant difference was seen in prevalence or in angle of septal deviation between groups. Prevalence of caudal aberrant nasal turbinates was significantly higher in brachycephalic dogs. CLINICAL SIGNIFICANCE: Nasal mucosal contact and caudal aberrant nasal turbinates were significantly more prevalent in brachycephalic dogs than in normocephalic dogs in our study. Computed tomography can be a valuable aid in obtaining data on nasal mucosal contact, caudal aberrant nasal turbinates and septal deviations. Combination of computed tomography with endoscopy and functional airway testing would be useful to further evaluate the correlation between intranasal features and symptoms of brachycephalic airway syndrome.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".