Hematologic differences between Dachshunds and mixed breed dogs
Bibliographic record
Abstract
BACKGROUND: Healthy Dachshunds, like Greyhounds, are reported to have a higher HCT than other dog breeds; however, there appears to be no objective information to support this observation. OBJECTIVE: The purpose of this study was to determine whether RBC counts, indices, and total and differential WBC counts differ between Dachshunds and mixed breed dogs. METHODS: In this retrospective study, CBC data and total solids were compared between 61 healthy Dachshunds and 60 mixed breed dogs that were presented for health check, dental prophylaxis, or neutering to a university and a private clinic. RESULTS: Dachshunds had higher mean PCV (52% vs 50%; P = .047), mean HCT (52% vs 48%; P = .0003), mean RBC count (7.7 × 10(6) /μL vs 7.1 × 10(6) /μL; P = .0004), and mean HGB concentration (18.2 g/dL vs 16.8 g/dL; P = .0003) than mixed breed dogs. There were slight differences in HCT and HGB concentration between clinics (P < .05). There was no evidence of a difference in MCV, MCHC, and total solids between breeds (P > .5). More Dachshunds than mixed breed dogs had RBC variables above the reference interval: 29% vs 2% for HCT (P = .001); 40% vs 7% for HGB concentration (P = .0006); and 26% vs 5% for RBC count (P = .01). There were statistically significant but clinically unimportant differences in differential WBC counts. CONCLUSIONS: Compared with mixed breed dogs, Dachshunds have higher PCV, HCT, RBC count, and HGB concentration. Veterinarians should consider these differences when interpreting CBCs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".