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Impact of sample volume and handling time during analysis on the in vitro quality measurements of platelet concentrates held in syringes

2011· article· en· W1583738894 on OpenAlexaff
Peter Schubert, Brankica Culibrk, Danielle Coupland, E.J. Levin, Dana V. Devine

Bibliographic record

VenueInternational Journal of Laboratory Hematology · 2011
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsCanadian Blood ServicesUniversity of British Columbia
Fundersnot available
KeywordsIn vitroPlateletChemistrypCO2PhosphatidylserinePlateletpheresisChromatographyBiomedical engineeringAndrologyApheresisImmunologyMedicineBiochemistryInternal medicineMembrane

Abstract

fetched live from OpenAlex

INTRODUCTION: The determination of quality parameters is a necessity for monitoring the efficacy of platelet concentrates. During consolidated quality control studies, there may be a large number of samples to be analyzed at the same time. This common workflow setup triggered the question whether there is an influence of the number of samples to be analyzed on the accuracy of the test results. METHODS: Two different sample volumes of platelet concentrates, 1 ml and 50 ml, were analyzed for a set of standard in vitro parameters including pCO(2), pO(2), pH, glucose, and lactate as well as platelet activation via CD62P expression and responsiveness to adinosine diphosphate in an extent-of-shape-change assay. To assess apoptotic mechanisms triggered by the hold time, changes in the phosphatidylserine exposure were monitored. RESULTS: In total, eleven time points were assessed over a 3-h period as well as an overnight point for assay evaluation. Except for pCO(2) and pO(2), all in vitro parameters analyzed were unaffected by a sample hold time of up to 3-h. CONCLUSION: Sampling for pO(2) determination should be carried out in small volumes and assessed within 30 min of collection to obtain reliable and comparable results.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score0.209

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.044
GPT teacher head0.325
Teacher spread0.281 · 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

Citations5
Published2011
Admission routes1
Has abstractyes

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