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Record W2036276670 · doi:10.1111/vcp.12178

Hematologic differences between Dachshunds and mixed breed dogs

2014· article· en· W2036276670 on OpenAlexaboutno aff
Ahmira R. Torres, Stephen E. Cassle, Michael Haymore, Richard C. Hill

Bibliographic record

VenueVeterinary Clinical Pathology · 2014
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsnot available
Fundersnot available
KeywordsBreedMedicineVeterinary medicineLabrador RetrieverInternal medicineAnimal scienceBiologyPathology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.885

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.221
GPT teacher head0.454
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2014
Admission routes1
Has abstractyes

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