Accuracy of point-of-care crossmatching methods and crossmatch incompatibility in critically ill dogs
Bibliographic record
Abstract
BACKGROUND: The performance of commercial point-of-care crossmatch (CM) tests compared to laboratory tube agglutination CM is unknown. Additionally, there is limited information regarding CM incompatibility in ill dogs. OBJECTIVES: To determine if point-of-care major CM methods are accurate in detecting compatible and incompatible tests when compared to laboratory CM methods, and to identify factors associated with CM incompatibility in dogs. ANIMALS: Part 1 (prospective) included 63 client-owned dogs potentially requiring blood transfusion. Part 2 (retrospective) included all dogs from part 1, plus medical records of 141 dogs with major CM results. METHODS: For part 1, major CM was performed using a tube agglutination assay (LAB-CM), a gel-based point-of-care test (GEL-CM), and an immunochromatographic point-of-care test (IC-CM). For part 2, medical record data were collected to determine rates of and risk factors for CM incompatibility. RESULTS: Kappa agreement between the LAB-CM and GEL-CM methods could not be calculated due to a relative lack of incompatible results. Kappa agreement between the LAB-CM and IC-CM methods was 0.16 (95% confidence interval [CI] = 0-0.31, P = .007) indicating no agreement. The LAB-CM incompatibility in transfusion-naïve vs dogs that had a transfusion was 25% and 35%, (P = .3). CONCLUSIONS AND CLINICAL IMPORTANCE: Compared to laboratory methods, point-of-care methods evaluated in our study lacked sensitivity for detecting incompatibilities. Dogs had similar rates of major CM incompatibility regardless of transfusion history. This suggests CM testing prior to transfusion be considered in all dogs however our study did not investigate clinical relevancy of incompatible LAB-CM.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".