Bibliographic record
Abstract
Flow cytometry is a highly sensitive and specific method for simultaneous analysis of multiple parameters of individual cells in a suspension. It has a range of applications in veterinary medicine, and it is increasingly used in veterinary oncology as more species-specific antibodies are generated and cross-reactivity of antibodies is characterized. Two major applications in veterinary oncology are (1) immunophenotyping with a panel of fluorescently labeled antibodies to assess expression of cell markers and (2) determination of the DNA content of cells with fluorescent dyes that bind nucleic acids. The diagnostic and prognostic value of classifying round cell tumors of animals-especially, lymphocyte proliferations-remains to be fully determined, but studies to date have indicated benefit to patient management. Similarly, determining the proliferating fraction of tumors through DNA analysis remains to be standardized and validated in veterinary oncology but shows promise as an adjunct to morphologic tumor classification. This article reviews technical aspects of flow cytometry, availability of antibodies suitable for studies in domestic animals, and applications in veterinary oncology with emphasis on characterization of round cell tumors.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".