Feline urinary incontinence: a retrospective case series (2009–2019)
Bibliographic record
Abstract
OBJECTIVES: The objective of this retrospective study was to describe the feline population presented for urinary incontinence at a veterinary teaching hospital between 2009 and 2019, with a particular focus on cats with a non-neurologic underlying cause. METHODS: The medical records of cats diagnosed with urinary incontinence were retrospectively evaluated. Signalment, clinical presentation, results of diagnostic tests, diagnosis of the underlying cause and treatments were recorded. When information was available, outcome was recorded and follow-ups divided into three time frames (0-1 week, 1 week to 3 months and >3 months). RESULTS: Thirty-five cats were presented with urinary incontinence. Of these, 18 cats with complete medical records presented urinary incontinence of non-neurologic origin. The most common clinical signs at presentation were urine leakage while resting (12/18), urine-soiled perineum (8/18), urine dribbling (8/18) and no spontaneous micturition (5/18). The most common underlying cause was urethral obstruction (67%; 12/18), with a majority due to urethral strictures (58%; 7/12). Other causes were suspected inflammation (2/12), neoplasia (1/12), urolithiasis (1/12) and foreign body (1/12). In 8/10 cats in which it was performed, cystoscopy and contrast cystourethrography were the methods that led to the diagnosis. Twelve cats with urethral obstruction underwent interventional procedures, resulting in complete resolution of incontinence in 7/12 and improvement in 1/12. Urinary tract infection was a common complication after 3 months (4/18). CONCLUSIONS AND RELEVANCE: When incontinence of non-neurologic origin is suspected in a cat, urethral obstruction should be considered. Advanced imaging studies (cystoscopy and contrast studies) are useful for diagnosis. A good prognosis was reported in cats undergoing interventional procedures with no long-term treatment.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".