Platelet Indices and Erythrocyte Parameters in Healthy Police Dogs in Khartoum State – Sudan
Bibliographic record
Abstract
Normal levels of platelets and erythrocytes parameters and the effect of breed and sex on them in healthy police dogs were determined. Forty-six dogs were used; 20 Labrador Retriever and 26 German Shepherd dogs comprising 21 males and 25 females, 20-83 months old. Five ml blood samples were collected from the cephalic vein; and analyzed using BK6100 Auto Hematology Analyzer. German Shepherd dogs showed significantly (p≤0.05) higher values than Labrador Retriever dogs for Mean platelet volume (MPV) (10.43±0.71vs. 9.9±0.73 FL), Red blood cells count (RBCs) (6.75±0.26 vs. 6.51±0.27 x1012 /L), and Hemoglobin concentration (HGB) (180.81±9.53vs.172.4±11.98 g/L), respectively. Highly positive significant correlations (p≤0.01) were found between Platelet count (PLT) and Plateletcrit (PCT) in males, females, and all dogs, and between MPV and Platelets distribution width (PDW) in all dogs. A highly positive significant correlation (p≤0.01) was found between MPV and PCT in all dogs. A highly negative significant correlation (p≤0.01) was found between PDW and RBCs and Red cell blood distribution width- index (RDW-CV) in males and all dogs. Also, a highly positive significant (p≤0.01) correlation was found between PDW and Mean corpuscular volume (MCV) in all dogs. A highly significant negative correlation (p≤0.01) was found between MPV and RBCs in males. A highly negative significant correlation (p≤0.01) was found between MPV and RDW (CV) in males. However, highly positive significant correlations (p≤0.01) were found between MPV and Hematocrit (HCT) in females. The breed may influence platelets and erythrocyte parameters and this should be considered in clinical interpretations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".