Diagnostic testing for feline panleukopenia in a shelter setting: a prospective, observational study
Bibliographic record
Abstract
Objectives The aim of this study was to optimize the diagnosis of feline panleukopenia virus (FPV) infection in a shelter setting by: (1) comparing the results of the canine parvovirus IDEXX SNAP Parvo (SNAP) point-of-care ELISA with a commercial FPV quantitative real-time PCR (qPCR) test; (2) assessing whether vomit and anal/rectal swabs could be used for early diagnosis; and (3) clarifying the interpretation of weak-positive SNAP test results. Methods The study included shelter cats and kittens with incomplete or unknown vaccination history that had clinical signs suspicious for feline panleukopenia and fecal SNAP and PCR tests performed within 24 h of onset. Feces, anal/rectal swabs and vomit were tested using SNAP and PCR, with fecal PCR utilized as the reference standard. Results One hundred and forty-five cats were included. Seventeen were diagnosed with FPV infection and 62 were negative; 66 could not be individually designated because they were co-housed. Sensitivity was as follows: fecal SNAP 55% (n = 102; 95% confidence interval [CI] 32–77); swab SNAP 30% (n = 55; 95% CI 7–65); swab PCR 77% (n = 55; 95% CI 46–95); and vomit PCR 100% (n = 17; 95% CI 16–100). Specificity was high (96–100%) for all sample and test types. For PCR-positive fecal samples, true-positive SNAP tests (including weak positives) had significantly higher DNA viral copy numbers than false-negative SNAP tests ( P = 0.0031). Conclusions and relevance The SNAP ELISA should be viewed as an initial diagnostic test to rule in feline panleukopenia. Positive fecal SNAP test results, including weak positives, are highly likely to be true positives in clinically affected animals. Negative results in clinically affected animals are unreliable and should be followed up with PCR testing.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".