Accuracy of polymerase chain reaction assays for diagnosis of feline immunodeficiency virus infection in cats
Bibliographic record
Abstract
OBJECTIVE: To determine the sensitivity, specificity, and overall diagnostic accuracy of polymerase chain reaction (PCR) assays offered by commercial diagnostic laboratories for diagnosis of FIV infection in cats. DESIGN: Prospective clinical trial. ANIMALS: 124 cats. PROCEDURE: Blood was collected from cats that were neither infected with nor vaccinated against FIV, uninfected cats that were vaccinated with a licensed FIV vaccine, and cats experimentally and naturally infected with FIV representing subtypes A, B, and C. Coded blood samples were submitted to 3 laboratories in the United States and Canada offering PCR assays for diagnosis of FIV infection to veterinary practitioners. All laboratories tested fresh blood samples, and 1 laboratory also tested samples submitted as dried blood smears. The FIV infection status in all cats was confirmed by virus isolation. Sensitivity, specificity, and correct results were calculated for each PCR assay. RESULTS: Sensitivity ranged from 41% to 93%. Specificity ranged from 81% to 100% in unvaccinated cats and 44% to 95% in cats vaccinated against FIV. Correct results were obtained in 58% to 90% of 124 cats tested. All tests misidentified both uninfected and infected cats. False-positive results by all laboratories were higher in cats vaccinated against FIV than in unvaccinated cats, suggesting that vaccination interferes with the performance or interpretation of PCR assays used for diagnosis of FIV infection. CONCLUSIONS AND CLINICAL RELEVANCE: PCR assays used for diagnosis of FIV infection presently marketed to veterinary practitioners in North America vary significantly in diagnostic accuracy and did not resolve the diagnostic dilemma resulting from vaccination of cats against FIV.
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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 | 0.014 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".