Discordant FeLV p27 immunoassay and PCR test results in 21 cats with hematologic disorders
Bibliographic record
Abstract
CASE SERIES SUMMARY: A total of 1692 medical records from a primary care feline practice and a veterinary referral hospital were evaluated retrospectively to assess discordant feline leukemia virus (FeLV) test results. In total, 73 cats were positive for FeLV using serum in a lateral flow immunoassay (LFI) or laboratory-based ELISA. Of these cats, 21 subsequently tested negative for FeLV proviral DNA by non-quantitative PCR on EDTA whole blood (16/21, 76.2%), bone marrow (4/21, 19%) or both (1/21, 4.7%). The proportional morbidity (an estimate of prevalence in a sample of the total population) for FeLV by LFI/ELISA and PCR assays was 3.1%, consistent with that reported in previous studies for cats in North America. Cats with discordant LFI/ELISA and PCR results had either primary bone marrow disease (18 autoimmune, one neoplastic), a bone marrow insult (hemotrophic mycoplasmosis) or systemic inflammation (pyothorax with a marked neutrophilic leukocytosis). The percentage of cats with a positive LFA/ELISA result and negative PCR assay surviving to discharge was 85.7% (18/21). Of these, 88.9% (16/18) survived 4 months to 6 years. Seven cats (33.3%) were re-tested with LFI or ELISA once primary disease was controlled, and all tested negative. RELEVANCE AND NOVEL INFORMATION: These findings indicate that in cats with bone marrow disease that shares features of progressive FeLV infection, positive LFI and ELISA FeLV test results should be followed up with FeLV proviral DNA PCR testing, particularly in populations where disease prevalence is low.
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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 | 0.003 |
| 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 | 0.001 | 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".