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Blockchain Traceability in Healthcare: Blood Donation Supply Chain

2021· article· en· W3134356917 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsTraceabilitySupply chainBlockchainBusinessImmutabilityLeverage (statistics)Smart contractHealth careProcess managementInformation sharingComputer scienceComputer securityRisk analysis (engineering)Marketing

Abstract

fetched live from OpenAlex

To support effective supply chain management (SCM) is a challenging issue for healthcare sectors. In healthcare, the requirements of blood to be fulfilled on demands are always directly or indirectly connected to its supply chain. For that, an effective blood supply chain system is required in which blood relevant information will be traceable at each stage of the blood supply (e.g., from donor to blood recipient), with trust and safety in testing, storage, and distribution phases and to keep the privacy of each donor. This study uses a Blockchain Ethereum platform as a solution to leverage traceability in the blood donation supply chain (BDSC). Blockchain is a highly efficient, decentralized, and peer-to-peer distributed technology deploys to provide end-to-end traceability, safety, immutability, and security in the BDSC ecosystem. As a part of this study, a role-based smart contract solution is used to define the access per each role, which therefore assists to ensure traceability and security of information in the BDSC ecosystem.

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.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score0.480

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.242
Teacher spread0.232 · 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