Feline blood genotyping versus phenotyping, and detection of non‐<scp>AB</scp> blood type incompatibilities in <scp>UK</scp> cats
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to determine the agreement between AB blood phenotyping and genotyping and determine whether non-AB blood type incompatibilities exist in UK cats. METHODS: Blood samples underwent phenotyping (A, B or AB) using microplate agglutination, and genotyping (AA, Ab or bb) using pyrosequencing of a fragment of the cytidine monophospho-N-acetylneuraminic acid hydroxylase gene. Non-AB blood type incompatibilities were investigated by cross-matching against reference blood of the same phenotype. RESULTS: Of 112 cats tested, 86 (77%) were blood phenotype A, 19 (17%) type B and 7 (6%) type AB. Genotype and initial phenotype agreed in 96% (107 of 112) of cats, but 5 were discordant; these were all B phenotype with either AA (n=2) or Ab (n=3) genotype. Two of the five cats had repeat blood samples tested: one was reclassified as phenotype A; the other remained phenotype B. Two cats had incompatibilities on minor cross-match, but these were attributed to phenotyping errors. CLINICAL SIGNIFICANCE: Unknown mutation(s) associated with phenotype B, resulting in false AA or Ab genotyping, were evident in a small number of cases in this study. No conclusive evidence for non-AB blood type incompatibilities was found.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".