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Comprehensive survey of red blood cell unit life cycle at a large teaching institution in eastern Canada

2009· article· en· W2099512291 on OpenAlexaffabout
Calvino Cheng, Dawn Trethewey, Irene Sadek

Bibliographic record

VenueTransfusion · 2009
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsGreenfield Research (Canada)Dalhousie University
Fundersnot available
KeywordsEconomic shortageBlood transfusionMedicineReceiptNova scotiaRed blood cellTransfusion medicineUnit (ring theory)Emergency medicinePediatricsSurgeryInternal medicineBusinessGeographyMathematicsAccounting

Abstract

fetched live from OpenAlex

BACKGROUND: Recent blood shortages and the potential clinical impact of red blood cell (RBC) age highlight the need to understand blood supply delivery. This study addresses the characteristics and mechanics of RBC unit trafficking and storage across the transfusion service, previously undescribed in the literature. STUDY DESIGN AND METHODS: This retrospective qualitative institutionwide survey assessed the comprehensive RBC life cycle within Capital District Health Authority in Nova Scotia, Canada, during 2007. RESULTS: A total of 15,930 unique RBC units were received from the supplier with 98.6% having mean age of 12.5 days. The mean ages on receipt, transfusion, and time spent in the transfusion system before transfusion were 12, 19.6, and 7.5 days, respectively. Of 12,298 units, 9689 (78.8%) remained within the blood transfusion services (BTSs), while 2609 (21.2%) migrated having been returned a mean of 1.26 times from locations outside BTS (SD, 0.56 times), the latter spending a mean of 18.4 hours outside the BTS. Stationary units had mean age at dispense and time spent in the transfusion system of 19.2 and 6.9 days, respectively, compared to migratory units at 21.3 and 9.9 days, respectively (p < 0.005). A total of 5.2% (636/12258) of units were discarded; 423 of 636 (66.5%) were due to "unit expiry." CONCLUSION: Quantification of our institutional RBC life cycle highlighted key areas for intervention, such as the reduction of unit migration, especially D- units, as this increased RBC age at transfusion. In the era of limited resources, this method could identify areas where effort should be directed to address unnecessary wastage and aging of RBC units.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.589

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)0.0000.000

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 head0.023
GPT teacher head0.254
Teacher spread0.231 · 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
Published2009
Admission routes2
Has abstractyes

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