Comparison of sedated respiratory-gated computed tomography to anesthetized inspiratory-expiratory breath-hold computed tomography in dogs with respiratory disease
Bibliographic record
Abstract
Objective: To determine the diagnostic accuracy of sedated respiratory-gated (RG) CT as a minimally invasive surrogate for anesthetized, ventilator-assisted inspiratory-expiratory breath-hold (I:E-BH) CT scans by being able to identify CT lung patterns and subpatterns, abnormal and normal lung attenuation scores, and bronchomalacia (BM) in dogs with respiratory disease. Methods: Sedated RG CT and anesthetized I:E-BH CT images were sequentially acquired in 50 client-owned dogs with respiratory clinical signs. Computed tomography lung patterns and subpatterns, 2 CT severity scores, and the presence of BM were assessed. Agreement was estimated using unweighted and weighted Cohen κ coefficients and Bland-Altman plots. McNemar tests and mixed-effects logistic or ordinal logistic regressions were utilized to evaluate discordant pairs between CT techniques. Results: Both scan types had minimal motion artifact. Nodular pattern and airspace nodules and reticular and subpleural interstitial thickening subpatterns were significantly underestimated in RG CT compared to I:E-BH CT. A fair agreement was estimated between the 2 methods for normal lung scores but not for abnormal lung scores, and for BM. There was a significant difference between the two techniques in diagnosing clinical BM. Inspiratory-expiratory breath-hold CT yielded more BM diagnoses, especially in younger dogs with milder BM scores. Conclusions: In dogs with respiratory disease, RG CT had good concordance with I:E-BH CT on most major lung patterns but not some nodular and linear subpatterns, underdetecting subtle but clinically relevant lesions. The probability of diagnosing clinical BM is higher with I:E-BH CT, particularly in milder cases. Clinical Relevance: Compared to RG CT, anesthetized I:E-BH CT provides superior technique and detail that allows for characterization of some subpatterns, which could have diagnostic implications.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".