Objective assessment of computed tomographic pulmonary attenuation of inspiratory and expiratory series in dogs with and without bronchomalacia
Bibliographic record
Abstract
OBJECTIVE: To document objective metrics of attenuation of the pulmonary parenchyma on inspiratory and expiratory breath-hold CT in dogs with bronchomalacia (BM) and dogs without BM (NoBM) using automated software analysis. Metrics included mean lung attenuation, percent low-attenuation area at -856 HU, percent high-attenuation area at -700 HU, and percent attenuation area between -600 and -250 HU. ANIMALS: Client-owned dogs with BM (n = 123) and NoBM (20). METHODS: This retrospective study utilized 3D Slicer software (Brigham and Women's Hospital) to assess pulmonary CT attenuation. Analysis used Spearman correlation and 2-way ANOVA with beta regression. RESULTS: Comparing the difference between inspiratory and expiratory phases, there was a significantly greater increase in mean lung attenuation (P = .001), a significant reduction in percent low-attenuation area at -856 HU (P = .016), and a significant increase in percent high-attenuation area at -700 HU and percent attenuation area between -600 and -250 HU (P < .001 and P < .0001, respectively) in BM versus NoBM dogs. CONCLUSIONS: The higher inspiratory and expiratory difference in lung attenuation in BM compared to NoBM dogs supports the presence of impaired parenchymal aeration downstream of segmental and subsegmental airway collapse. CLINICAL RELEVANCE: Quantitative image analysis holds promise for objectively evaluating changes with BM.
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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 | 0.001 | 0.000 |
| 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) | 0.001 | 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".