COMPARISON OF SINGLE‐SLICE COMPUTED TOMOGRAPHY PROTOCOLS FOR DETECTION OF PULMONARY NODULES IN DOGS
Bibliographic record
Abstract
Two dogs (4 and 38 kg) with radiographic evidence of pulmonary nodules were evaluated using single-slice, helical computed tomography (CT). Each thorax was scanned using 12 combinations of examination parameters that included slice collimation width (3 and 5mm for the small dog and 5 and 7mm for the large dog), pitch (1, 1.5, and 2), and reconstruction interval (0.5 and 1). Sensitivity, specificity, and accuracy for nodule detection were evaluated for each protocol by three different observers, their results being compared with a consensus evaluation of images acquired with the protocol providing the best theoretic resolution (narrow collimation, pitch of 1, reconstruction interval of 0.5). For all observers, sensitivity and accuracy were significantly increased when using a protocol with narrow collimation (P < 0.0001-0.005 and P = 0.0003-0.005, respectively). Pitch and reconstruction interval did not significantly influence the accuracy, sensitivity, or specificity for at least two of the observers. Additionally, nodule size (< 3mm vs. > 3mm) did not significantly affect nodule detection. Interobserver repeatability was variable among protocols (K = 0.32-0.78), highlighting the fact that nodule detection may be more dependent on the observer than on the choice of the CT protocol. For single-slice CT, the results of this study suggest that narrow collimation (3-5 mm, depending on the animal's size), a pitch of 2 and a reconstruction interval of 1 should be used in dogs for the detection of pulmonary nodules.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".