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Record W2108916902 · doi:10.1155/2013/154838

Process Improvement by Eliminating Mixing of Whole Blood Units after an Overnight Hold Prior to Component Production Using the Buffy Coat Method

2013· article· en· W2108916902 on OpenAlexafffundabout
Cherie Mastronardi, Peter Schubert, E.J. Levin, Varsha Bhakta, Qilong Yi, Adele Hansen, Tamiko Stewart, Craig Jenkins, Wanda Lefresne, William P. Sheffield, Jason P. Acker

Bibliographic record

VenueJournal of Blood Transfusion · 2013
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsUniversity of British ColumbiaMcMaster UniversityCanadian Blood Services
FundersCentre for Blood Research, University of British ColumbiaCanadian Blood Services
KeywordsBuffy coatHemolysisBlood componentHematocritHemoglobinPlateletCentrifugationWhole bloodMixing (physics)Yield (engineering)ChemistryBiomedical engineeringChromatographyMedicineSurgeryMaterials scienceInternal medicineBiochemistryEmergency medicine

Abstract

fetched live from OpenAlex

The elimination of a thorough manual mixing of whole blood (WB) which takes place following the overnight hold, but before the first centrifugation step, during buffy coat component production at Canadian Blood Services (CBS) was investigated. WB was pooled after donation and split. Pairs of platelet, red blood cell (RBC), and plasma components were produced, with half using the standard method and half using a method in which the mixing step was eliminated. Quality assessments included yield, pH, CD62P expression and morphology for platelets, hemoglobin, hematocrit, hemolysis, and supernatant K(+) for RBCs, and volume and factor VIII activity levels for plasma. All components, produced using either method, met CBS quality control criteria. There were no significant differences in platelet yield between components produced with and without mixing. A significant difference was seen for RBC hemolysis at expiry (P = 0.03), but for both groups, levels met quality control requirements. Noninferiority of components produced without mixing was confirmed for all parameters. Manual mixing is laborious and has a risk of repetitive strain for production staff and its significance is unclear. Elimination of this step will improve process efficiencies without compromising quality.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.285
Teacher spread0.268 · 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

Citations5
Published2013
Admission routes3
Has abstractyes

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