A COMPARISON OF COMPUTED TOMOGRAPHY, COMPUTED RADIOGRAPHY, AND FILM‐SCREEN RADIOGRAPHY FOR THE DETECTION OF CANINE PULMONARY NODULES
Bibliographic record
Abstract
Computed tomography (CT) has become more widely available and computed radiography (CR) has replaced film-screen radiography for canine thoracic imaging in many veterinary practices. There are limited data comparing these modalities in a veterinary clinical setting to detect pulmonary nodules. We compared CT, CR, and film-screen radiography for detecting the presence, number, and characteristics of pulmonary nodules in dogs. Observer performance for a variety of experience levels was also evaluated. Twenty-one client-owned dogs with a primary neoplastic process underwent CT and CR; nine also received film-screen radiographs. Positive/negative classification by consensus agreed between the three modalities in 8/9 dogs and between CR and CT in the remaining 12. CT detected the greatest (P = 0.002) total number of nodules and no difference was seen between CR and films. The greatest number of nodules was seen in the right middle and both caudal regions, but only using CT (P < 0.0001). Significantly smaller nodules were detected with CT (P = 0.0007) and no difference in minimum size was detected between CR and films. Observer accuracy was high for all modalities; particularly for CT (90.5-100%) and for the senior radiologist (90.5-100%). CT was also characterized by the least interobserver variability. Although CT, CR, and film-screen performed similarly in determining the presence or absence of pulmonary nodules, a greater number of smaller nodules was detected with CT, and CT was associated with greater diagnostic confidence and observer accuracy and agreement.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".