Variation in mineral types of uroliths from ferrets (Mustela putorius furo) submitted for analysis in North America, Europe, or Asia over an 8-year period
Bibliographic record
Abstract
OBJECTIVE: ) from North America, Europe, and Asia and to identify potential risk factors associated with cystine urolithiasis in ferrets. SAMPLES: 1,054 laboratory submission records of uroliths obtained from ferrets between January 1, 2010, and December 31, 2018. PROCEDURES: For this cross-sectional study, the medical records databases at 4 diagnostic laboratories were searched for records of submissions of uroliths obtained from ferrets. Data collection included submission date; ferret sex, neuter status, and age; receiving laboratory and continent; and urolith mineral type. Regression analyses were performed to identify variables associated with cystine uroliths. RESULTS: Of the 1,054 urolith submissions, 1,013 were from North America, with 92.6% (938/1,013; 95% CI, 90.8% to 94.1%) cystine uroliths, and 41 were from Europe and Asia, with only 26.8% (11/41; 95% CI, 15.7% to 41.9%) cystine uroliths. Median age was 2.0 years for ferrets with cystine urolithiasis versus 4.0 years for those with other types of uroliths. Submissions were more likely cystine uroliths for ferrets in North America versus Europe and Asia (adjusted OR [aOR], 59.5; 95% CI, 21.4 to 165.6), for ferrets that were younger (aOR, 0.67; 95% CI, 0.58 to 0.77), or for submissions in 2018 versus 2010 (aOR, 21.1; 95% CI, 5.1 to 87.9). CONCLUSIONS AND CLINICAL RELEVANCE: Results indicated that the proportion of submissions that were cystine uroliths dramatically increased in North America between 2010 and 2018. There is an urgent need to determine underlying causes and mitigate cystine urolithiasis in ferrets.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".