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Effects of in vitro hemodilution of canine blood on platelet function analysis using the PFA‐100

2009· article· en· W2061487096 on OpenAlexaff
Noel Clancey, Shelley Burton, Barbara S. Horney, Allan MacKenzie, Andrea Nicastro, Étienne Côté

Bibliographic record

VenueVeterinary Clinical Pathology · 2009
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsPlateletMedicineWhole bloodAnalysis of variancePlatelet-poor plasmaNuclear medicinePlatelet-rich plasmaInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The platelet function analyzer (PFA)-100 is a point-of-care instrument previously evaluated in humans and dogs. In both species, artificially prolonged platelet closure time (CT) occurs with anemia. Reliability of the analyzer in dogs becomes a concern when the HCT is between 0.25 and 0.35 L/L. OBJECTIVE: The objective of this study was to further define the level of HCT at which CT is prolonged, using in vitro diluted canine blood. METHODS: Citrated whole blood samples were collected from 22 healthy dogs. Initial HCT was determined and autologous platelet-rich plasma was added to samples to achieve HCTs of 0.33, 0.30, and 0.27 L/L. CT was determined in duplicate on the PFA-100 using collagen/adenosine-5'-diphosphate cartridges. RESULTS: Compared with the initial CT in samples with HCT 0.39-0.54 L/L (CT mean+/-SD=57.8+/-5.75 seconds), significantly prolonged CTs were found in hemodiluted samples with HCT 0.33 L/L (61.1+/-4.64 seconds), 0.30 L/L (64.3+/-6.79 seconds), and 0.27 L/L (70.8+/-7.90 seconds) (P=0.029; repeated measures ANOVA). CONCLUSION: Although statistical differences were found, further studies are needed to determine the clinical significance of the mild prolongation in CT associated with mild anemia. Until then, dogs with HCTs slightly <0.35 L/L should be evaluated cautiously for platelet dysfunction using the PFA-100.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.926
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.051
GPT teacher head0.360
Teacher spread0.309 · 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 designBench or experimental
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

Citations9
Published2009
Admission routes1
Has abstractyes

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