The most common types of uroliths larger than 1 mm are readily visible and accurately measured in an in vitro setting mimicking the canine abdomen using digital radiography
Bibliographic record
Abstract
Abdominal radiography is an important diagnostic to detect uroliths. Cystine and urate uroliths were historically characterized as nonmineral opaque on survey radiographs. However, recent research and clinical observations indicate that pure urate and cystine uroliths may be detected with digital radiography. The primary purpose of this prospective, in vitro, diagnostic accuracy study was to determine the sensitivity of digital radiography in detecting cystoliths of varying size and composition. Forty canine uroliths of pure composition (10 each of calcium oxalate, struvite, cystine, and urate), acquired from Minnesota Urolith Center and ranging from 1 to 10 mm, were placed in phantoms of three various sizes and radiographed. The radiographs, including three sets of each urolith separately, were evaluated by three blinded radiologists on two separate occasions. Evaluation included presence or absence of urolith, number of uroliths, and maximum diameter of the urolith(s). For all four types of uroliths and all readers, the specificity and PPV were 100% with an associated very high sensitivity (94.4%-98.9%) and NPV (94.8%-98.9%). Calcium oxalate uroliths were the most accurately measured and struvite were the least accurately measured when compared with the gross measurement. Smaller uroliths were more accurately measured than larger uroliths. Uroliths placed in smaller bladder phantoms were more accurately measured than in larger bladder phantoms. Though accurate measurement of uroliths is complicated by and dependent on numerous variables, our results reveal that urate and cystine uroliths are visualized on digital radiography making them a relevant differential diagnosis when seen clinically.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".