Analytic characterization of flow cytometric assays for detection of immunoglobulin G on canine erythroid cells, including detection of dog erythrocyte antigen 1 on erythroid precursors
Bibliographic record
Abstract
OBJECTIVE To develop and characterize flow cytometric assays for detecting IgG bound to canine erythrocytes and bone marrow erythroid precursors. SAMPLE Blood samples from 20 healthy and 61 sick dogs with (n = 33) or without (28) immune-mediated hemolytic anemia (IMHA) and bone marrow samples from 14 healthy dogs. PROCEDURES A flow cytometric assay for measurement of IgG on RBCs was developed, and appropriate positive control cells were generated. Analytic and diagnostic performance were characterized. The RBC IgG assay was then combined with density-gradient fractionation of aspirated bone marrow cells and a 2-color process to yield an assay for detecting IgG on nucleated RBCs (nRBCs). Cell sorting and cytologic examination confirmed target cell populations, and anti-dog erythrocyte antigen 1 (DEA1) blood-typing serum was used to generate IgG-positive nRBCs. RESULTS Within- and between-run coefficients of variation for the RBC IgG assay were 0.1% to 13.9%, and > 90% of spiked IgG-positive RBCs were detected. Diagnostic sensitivity and specificity of the assay for detection of IMHA were 88% and 93%, respectively. Cytologic findings for sorted bone marrow fractions rich in early-, mid-, and late-stage nRBCs from 3 healthy dogs indicated 89% to 98% nRBC purity. After IgG coating with anti-DEA1 blood-typing serum, IgG was detected on nRBCs from DEA1-positive, but not DEA1-negative, healthy dogs. CONCLUSIONS AND CLINICAL RELEVANCE The developed RBC IgG assay had favorable analytic and diagnostic performance for detection of IMHA in dogs and was successfully adapted to detect IgG on canine nRBCs of various maturation stages. The findings supported the presence of DEA1 on canine nRBCs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".