Comparison of 3 Leukogram Determination Methods in Avian Species: Phloxine B Stain, Blood Smear, and an Automated Analyzer
Bibliographic record
Abstract
Because avian blood cells are nucleated, most automated methods used in mammalian species for total white blood cell (WBC) counts and differentials are considered inaccurate. Therefore, manual methods are routinely used in birds, although this could result in variations in methods across laboratories. The objective of this study was to evaluate and compare 3 methods of avian leukogram determination: a commercial phloxine B stain method (PB), estimation from a blood smear (EBS), and an automated analyzer (Cell Dyn 3500, [CD]). Leukograms from 23 avian blood samples were compared using these methods. All samples were evaluated once by 4 observers to assess the repeatability and precision of the manual methods (PB and EBS). Analyses with the CD method were repeated 5 times on 3 samples to evaluate repeatability. The WBC counts and differentials obtained with CD were compared to the 2 other methods by calculating intraclass correlation coefficients (ICC). Agreement between WBC counts from EBS and PB and between CD and PB was assessed with Bland-Altman plots. Results based on the CD analyzer correlated poorly with the other methods. When compared with the EBS method, ICCs ranged from 0–4.3% for heterophils, 0–12% for lymphocytes, 0–23.4% for monocytes, and were equal to 0% for eosinophils. When comparing the CD with PB, ICCs for WBC counts ranged from 85.9–91.5% among observers. High interobserver agreement was seen for the leukograms obtained with EBS (ICC = 92.9%). A good agreement was noted between EBS and PB for WBC counts (ICC = 69.5–81.3%). Bland Altman plots indicated good agreement for WBC counts between EBS and PB (slope P value = 0.52) and CD and PB (slope P value = 0.13). Although less precise than PB, EBS proved to be clinically useful and was both time and cost-efficient. The CD method does not seem adapted for avian leukocyte differentials.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".