Pseudomembranous cystitis in cats with presumed or confirmed mineralization: A retrospective study of 26 cases (2016-2021)
Bibliographic record
Abstract
BACKGROUND: Pseudomembranous cystitis (PMC) in cats is a recognized disease, but concurrent mineralization is reported rarely and its outcome is poorly described. HYPOTHESIS AND OBJECTIVES: Describe a population of cats with PMC and the prevalence of concurrent mineralization. ANIMALS: Twenty-six cats with PMC. METHODS: Medical records were retrospectively reviewed (January 2016 to December 2021). Cats with an ultrasound diagnosis of PMC were included. Clinicopathologic results, imaging, treatment, and outcome were reviewed. RESULTS: All cats were male and 21 (80%) were diagnosed with urethral obstruction (UO). Five cats (23.8%) had positive urine culture (Staphylococcus felis, 3/5; Proteus mirabilis, 2/5) with a median urine pH of 8 (range, 6-9). All cats had ultrasonographic changes suggestive of mineralization. On ultrasound examination, 10 cats (38.5%) had pseudomembranes with acoustic shadowing suggestive of mineralization, 15 (57.7%) had changes indicative of ulceration, and 8 (31%) had changes compatible with of a urachal anomaly. Twenty-two cats received medical treatment, 4 underwent surgery (3 percutaneous cystolithotomy, 1 cystotomy). Twenty cats (77%) survived to discharge. Follow-up ultrasound examination indicated resolution of PMC in 6/7 cats, 4 had persistent hyperechoic bladder lining. Five of 12 cats with follow-up had a relapse of lower urinary tract signs. CONCLUSIONS AND CLINICAL IMPORTANCE: Pseudomembranous cystitis was diagnosed mainly in male cats with UO and imaging findings suggestive of mineralization were present in all cases. Frequent negative urine culture suggests a different etiology than encrusting cystitis related to urease-positive bacteria. Good outcomes can be achieved with medical management.
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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 | 0.001 | 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".