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Validation of the Coulter AcT Diff Hematology Analyzer for Analysis of Blood of Common Domestic Animals

2000· article· en· W2138395516 on OpenAlexaff
Hilary Dawson, Brent Hoff, Evert Grift, Harold Tvedten, M. M. Shoukri

Bibliographic record

VenueVeterinary Clinical Pathology · 2000
Typearticle
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsHematology analyzerAutoAnalyzerHematologyMedicineCATSConcordanceVeterinary medicineCoulter counterHemoglobinInternal medicineBiology

Abstract

fetched live from OpenAlex

The objective of this study was to compare and assess the agreement between the Coulter AcT Diff hematology analyzer (CAD) and the Bayer Technicon H1 (H1) using blood samples from 391 animals of 4 species. The H1 has been used in veterinary laboratories for many years. Recently, Coulter modified the CAD and added veterinary software for hematologic analysis of feline, canine, and equine samples. A comparison of hemograms from dogs, cats, horses, and cattle was made using EDTA-anticoagulated blood samples. Both instruments were calibrated using human blood products. Performance characteristics were excellent for most values. The exceptions were MCV in canine samples (concordance correlation of.710), platelet counts for feline and equine samples (.258 and.740, respectively), feline and bovine WBC counts (.863 and.857, respectively), and bovine hemoglobin (.876).

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)0.003
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 score0.089
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.131
GPT teacher head0.480
Teacher spread0.350 · 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

Citations10
Published2000
Admission routes1
Has abstractyes

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