Impact on canine neutrophil preservation with the addition of bovine serum albumin to <scp>K<sub>3</sub>‐EDTA</scp> whole blood samples
Bibliographic record
Abstract
BACKGROUND: Cellular deterioration occurs with blood sample aging, impacting white blood cell (WBC) identification and differential accuracy. This may be exacerbated in samples from patients experiencing inflammation. Previously, bovine serum albumin (BSA) has been shown to improve cellular preservation of blood and other samples, but the effect on cell preservation in canine blood has not been assessed. OBJECTIVES: -EDTA)-anticoagulated canine blood prior to blood smear preparation. We evaluated the impact of inflammatory leukograms, sample storage temperatures (4° and 20°C), and time on outcomes. MATERIALS AND METHODS: -EDTA-anticoagulated blood samples stored at 4° and 20°C were used from unique patients, 10 with and 10 without inflammatory leukograms. Blood smears were prepared from aliquots with or without the addition of 22% BSA at 0, 4, 8, 24, 48, and 72 h. The nuclear area was measured for 25 randomly selected neutrophils per slide using Fiji software. Mixed-effect linear regression modeling was performed (significance: P < 0.05). RESULTS: Nuclear area increased over time with and without added BSA. Both sample storage temperatures and the presence or absence of an inflammatory leukogram significantly impacted neutrophil nuclear area. Samples with added BSA had slightly higher predicted nuclear areas than those without BSA, but this difference was not statistically significant. CONCLUSIONS: BSA did not significantly impact neutrophil nuclear area and did not improve neutrophil preservation in canine blood samples.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".