Automated hematologic analysis of bone marrow aspirate samples from healthy Beagle dogs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Interpretation of bone marrow (BM) smears typically is comprised of qualitative assessment and differential counting of cells. Analysis of BM fluid with automated hematology analyzers may provide rapid characterization of cells to supplement microscopic interpretation. OBJECTIVES: The purpose of the study was to examine the practicality and utility of analyzing BM samples in the Advia 2120 hematology analyzer; to determine if results correlate with smear assessment; and to establish descriptive statistics from hematologically normal and clinically healthy Beagle dogs. METHODS: Anticoagulated BM aspirates from 3 different sites of 26 adult Beagle dogs were collected. BM samples were analyzed in the Advia 2120, and numerical results were correlated with microscopic assessment of corresponding BM smears. Results from automated analyses and manual 500-cell differential counts were statistically analyzed. RESULTS: Forty-six samples were suitable for complete analysis. Results were available in approximately 2 (Advia) and 30 (stained and cover-slipped smear) minutes. Advia nucleated cell concentration was significantly correlated with microscopic assessment of smear particle number and smear cellularity. Significant correlations were also identified for Advia percent neutrophils with segmented, band and metamylocyte neutrophils, Advia percent lymphocytes with rubricytes, and Advia percent large unstained cells (LUC) with myeloblasts and promyelocytes. CONCLUSIONS: Automated analysis of BM aspirates was practicable, although techniques to obtain cellular samples and avoid clot formation could be improved. Automated analysis may provide rapid and useful preliminary information regarding sample cellularity, and granulocytic and erythrocytic components. Automated analysis should not supplant microscopic assessment, but may be a useful adjunct.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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 it