Feline leukaemia virus status of Australian cats with lymphosarcoma
Bibliographic record
Abstract
OBJECTIVE: To determine the FeLV status of sera and tumours from Australian cats with lymphosarcoma in relation to patient characteristics, tumour characteristics (tissue involvement, histological grade and immunophenotype), haematological and biochemical values. DESIGN: Prospective study of 107 client-owned cats with naturally-occurring lymphosarcoma. PROCEDURE: An ELISA was used to detect FeLV p27 antigen in serum specimens collected from cats with lymphosarcoma. A PCR was used to detect FeLV DNA in formalin-fixed, paraffin-embedded tissue sections containing neoplastic lymphoid cells. The PCR was designed to amplify a highly conserved region of the untranslated long terminal repeat of FeLV provirus. RESULTS: Only 2 of 107 cats (2%), for which serum samples were available, were FeLV-positive on the basis of detectable p27 antigen in serum. In contrast, 25 of 97 tumours (26%) contained FeLV DNA. Of the 86 cats for which both PCR and ELISA data were available, 19(22%) had FeLV provirus in their tumours but no detectable circulating FeLV antigen in serum, while 2 (2%) had FeLV provirus and circulating FeLV antigen. FeLV PCR-positive/ELISA-negative cats (19) differed from PCR-negative/ELISA-negative cats (65) in having fewer B-cell tumours (P = 0.06), more non B-/non T-cell tumours (P = 0.02) and comprising fewer non-Siamese/Oriental pure-bred cats (P = 0.03). CONCLUSIONS: The prevalence of FeLV antigen or provirus was considerably lower in our cohort of cats compared with studies of lymphosarcoma conducted in the Northern hemisphere. This suggests that factors other than FeLV are important in the development of lymphosarcoma in many Australian cats. No firm conclusions could be drawn concerning whether FeLV provirus contributed to the development of lymphosarcoma in PCR-positive/ELISA-negative 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".