Preliminary evaluation of a gel tube agglutination major cross‐match method in dogs
Bibliographic record
Abstract
BACKGROUND: A major cross-match gel tube test is available for use in dogs yet has not been clinically evaluated. OBJECTIVES: This study compared cross-match results obtained using the gel tube and the standard tube methods for canine samples. METHODS: Study 1 included 107 canine sample donor-recipient pairings cross-match tested with the RapidVet-H method gel tube test and compared results with the standard tube method. Additionally, 120 pairings using pooled sera containing anti-canine erythrocyte antibody at various concentrations were tested with leftover blood from a hospital population to assess sensitivity and specificity of the gel tube method in comparison with the standard method. RESULTS: The gel tube method had a good relative specificity of 96.1% in detecting lack of agglutination (compatibility) compared to the standard tube method. Agreement between the 2 methods was moderate. Nine of 107 pairings showed agglutination/incompatibility on either test, too few to allow reliable calculation of relative sensitivity. Fifty percent of the gel tube method results were difficult to interpret due to sample spreading in the reaction and/or negative control tubes. CONCLUSIONS: The RapidVet-H method agreed with the standard cross-match method on compatible samples, but detected incompatibility in some sample pairs that were compatible with the standard method. Evaluation using larger numbers of incompatible pairings is needed to assess diagnostic utility. The gel tube method results were difficult to categorize due to sample spreading. Weak agglutination reactions or other factors such as centrifuge model may be responsible.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".