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Record W4391238391 · doi:10.1111/vox.13590

The future of blood services amid a tight balance between the supply and demand of blood products: Perspectives from the <scp>ISBT</scp> Young Professional Council

2024· article· en· W4391238391 on OpenAlexaff
Antoine Lewin, Eunike C. McGowan, Jian Ou‐Yang, Lilian Antwi Boateng, Carla Luana Dinardo, Saikat Mandal, Nour AlMozain, Jannison Karlly Cavalcante Ribeiro, Praiseldy Langi Sasongko

Bibliographic record

VenueVox Sanguinis · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsUniversité de SherbrookeHéma-Québec
FundersNational Institute for Health and Care Research
KeywordsMedicineDeferralBlood transfusionBlood collectionBlood supplySupply and demandApheresisBalance (ability)Health professionalsTransfusion medicineBusinessIntensive care medicineHealth careImmunologyPlateletFinanceSurgeryMedical emergencyEconomic growthEconomicsPhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Blood services manage the increasingly tight balance between the supply and demand of blood products, and their role in health research is expanding. This review explores the themes that may define the future of blood banking. MATERIALS AND METHODS: We reviewed the PubMed database for articles on emerging/new blood-derived products and the utilization of blood donors in health research. RESULTS: In high-income countries (HICs), blood services may consider offering these products: whole blood, cold-stored platelets, synthetic blood components, convalescent plasma, lyophilized plasma and cryopreserved/lyophilized platelets. Many low- and middle-income countries (LMICs) aim to establish a pool of volunteer, non-remunerated blood donors and wean themselves off family replacement donors; and many HICs are relaxing the deferral criteria targeting racial and sexual minorities. Blood services in HICs could achieve plasma self-sufficiency by building plasma-dedicated centres, in collaboration with the private sector. Lastly, blood services should expand their involvement in health research by establishing donor cohorts, conducting serosurveys, studying non-infectious diseases and participating in clinical trials. CONCLUSION: This article provides a vision of the future for blood services. The introduction of some of these changes will be slower in LMICs, where addressing key operational challenges will likely be prioritized.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.624
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.010
GPT teacher head0.219
Teacher spread0.209 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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