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Record W2098827989 · doi:10.1111/trf.12303

Segments from red blood cell units should not be used for quality testing

2013· article· en· W2098827989 on OpenAlexaff
Jayme Kurach, Adele Hansen, Tracey R. Turner, Craig Jenkins, Jason P. Acker

Bibliographic record

VenueTransfusion · 2013
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsCanadian Blood ServicesUniversity of Alberta
Fundersnot available
KeywordsHemolysisBuffy coatHematocritHemoglobinFiltration (mathematics)Whole bloodAndrologyMedicineSurgeryMathematicsImmunologyInternal medicineStatistics

Abstract

fetched live from OpenAlex

BACKGROUND: Nondestructive testing of blood components could permit in-process quality control and reduce discards. Tubing segments, generated during red blood cell (RBC) component production, were tested to determine their suitability as a sample source for quality testing. STUDY DESIGN AND METHODS: Leukoreduced RBC components were produced from whole blood (WB) by two different methods: WB filtration and buffy coat (BC). Components and their corresponding segments were tested on Days 5 and 42 of hypothermic storage (HS) for spun hematocrit (Hct), hemoglobin (Hb) content, percentage hemolysis, hematologic indices, and adenosine triphosphate concentration to determine whether segment quality represents unit quality. RESULTS: Segment samples overestimated hemolysis on Days 5 and 42 of HS in both BC- and WB filtration-produced RBCs (p < 0.001 for all). Hct and Hb levels in the segments were also significantly different from the units at both time points for both production methods (p < 0.001 for all). Indeed, for all variables tested different results were obtained from segment and unit samples, and these differences were not consistent across production methods. CONCLUSION: The quality of samples from tubing segments is not representative of the quality of the corresponding RBC unit. Segments are not suitable surrogates with which to assess RBC 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)0.000
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 score0.163
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.141
GPT teacher head0.319
Teacher spread0.178 · 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

Citations19
Published2013
Admission routes1
Has abstractyes

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