Bibliographic record
Abstract
OBJECTIVE To determine whether dual-energy CT (DECT) could accurately differentiate the composition of common canine uroliths in a phantom model. SAMPLE 30 canine uroliths with pure compositions. PROCEDURES Each urolith was composed of ≥ 70% struvite (n = 10), urate (8), cystine (5), calcium oxalate (4), or brushite (3) as determined by standard laboratory methods performed at the Canadian Veterinary Urolith Centre. Uroliths were suspended in an agar phantom, and DECT was performed at low (80 kV) and high (140 kV) energies. The ability of low- and high-energy CT numbers, DECT number, and DECT ratio to distinguish uroliths on the basis of composition was assessed with multivariate ANOVA. RESULTS No single DECT measure differentiated all urolith types. The DECT ratio differentiated urate uroliths from all other types of uroliths. The DECT and low-energy CT numbers were able to differentiate between 8 and 7 pairs of urolith types, respectively. CONCLUSIONS AND CLINICAL RELEVANCE Results indicated that DECT was unable to differentiate common types of canine uroliths in an in vitro model; therefore, it is unlikely to be clinically useful for determining urolith composition in vivo. Given that the primary reasons for determining urolith composition in vivo are to predict response to shock wave lithotripsy and develop a treatment plan, future research should focus on the correlation between DECT measurements and urolith fragility rather than urolith composition.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".