Perspectives in veterinary medicine: Description and classification of bronchiolar disorders in cats
Bibliographic record
Abstract
This Perspectives in Veterinary Medicine article seeks to define, describe putative causes, and discuss key diagnostic tests for primary and secondary bronchiolar disorders to propose a classification scheme in cats with support from a literature review and case examples. The small airways (bronchioles with inner diameters <2 mm), located at the transitional zone between larger conducting airways and the pulmonary acinus, have been overlooked as major contributors to clinical syndromes of respiratory disease in cats. Because the trigger for many bronchiolar disorders is environmental and humans live in a shared environment with similar susceptibility, understanding these diseases in pet cats has relevance to One Health. Thoracic radiography, the major imaging modality used in the diagnostic evaluation of respiratory disease in cats, has low utility in detection of bronchiolar disease. Computed tomography (CT) with paired inspiratory and expiratory scans can detect pathology centered on small airways. In humans, treatment of bronchiolar disorders is not well established because of heterogeneous presentations and often late definitive diagnosis. A review of the human and veterinary medical literature will serve as the basis for a proposed classification scheme in cats. A case series of cats with CT or histopathologic evidence of bronchiolar lesions or both, either as a primary disorder or secondary to extension from large airway disease or interstitial lung disease, will be presented. Future multi-institutional and multidisciplinary discussions among clinicians, radiologists, and pathologists will help refine and develop this classification scheme to promote early and specific recognition and optimize treatment.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".