Perirenal hemorrhage associated with feline infectious peritonitis: a novel presentation of a classic disease
Bibliographic record
Abstract
), is a significant disease of felids. We investigated perirenal hemorrhage, an unreported lesion in FIP, through a retrospective analysis of 51 immunohistochemistry-confirmed FIP cases submitted to the Diagnostic Services Unit (DSU; University of Calgary, Calgary, Alberta, Canada) between 2010 June 30 and 2024 June 30. Five cats had perirenal hemorrhage in the right retroperitoneal space; 4 had concurrent subcapsular renal hemorrhage; and 1 had sublumbar muscle hemorrhage and hemoabdomen. One case had additional hemorrhages in the brain and cervical spinal cord. Concurrent gross lesions typical of FIP included pyogranulomatous inflammation in various organs and protein-rich cavitary effusions. Histologic lesions typical of FIP (vasculitis and pyogranulomatous inflammation) were present in the kidneys and retroperitoneal fat of 4 cases, and in 3 cases, FCoV antigen was demonstrated in the regions of hemorrhage. The exact mechanism of this hemorrhage is unknown, but we speculate that vasculitis caused by FIP is the cause. Despite the relatively low prevalence of perirenal hemorrhage in this cohort, this lesion represents a unique, previously unreported manifestation of FIP that clinicians and pathologists should be aware of and consider in the differential diagnosis for fluid accumulation or space-occupying lesions in the retroperitoneum of cats.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".