Associations between biochemical parameters and referral centre in pet rabbits with urolithiasis
Bibliographic record
Abstract
OBJECTIVES: To determine the association between signalment, selected haematologic and biochemical parameters and referral centre in pet rabbits with imaging evidence of urolithiasis presented to two veterinary teaching hospitals in North America. MATERIALS AND METHODS: The medical record database of two veterinary teaching hospitals was searched from 2009 to 2019 for records of pet rabbits that received both imaging studies and plasma biochemistry profiles. Information regarding signalment, bodyweight, packed cell volume, total solids, and plasma biochemistry profiles was obtained. Univariable and multivariable logistic regression models were performed to identify statistically significant parameters associated with imaging evidence of urolithiasis. RESULTS: Of the 324 examined rabbits, 33 (10.2%) had confirmed evidence of urolithiasis on imaging. Increasing plasma calcium and sodium concentrations and referral centre were significantly associated with the presence of urolithiasis on the univariable logistic regression model. However, only plasma calcium concentration and the referral centre demonstrated significant associations on the multivariable logistic regression model. CLINICAL SIGNIFICANCE: Results indicate that urolithiasis in pet rabbits that receive imaging is associated with mildly increasing plasma calcium concentration and referral centre. The association with referral centre may indicate there are geographic influences on urolithiasis or on imaging. However, the identified associations have low predictive value for the diagnosis of urolithiasis, indicating the need for additional diagnostic modalities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".