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Record W2138246948 · doi:10.1111/trf.12719

Current thawing and infusion practice of cryopreserved cord blood: the impact on graft quality, recipient safety, and transplantation outcomes

2014· review· en· W2138246948 on OpenAlexaff
Salem Akel, Donna Regan, Donna A. Wall, L. Petz, Jeffrey McCullough

Bibliographic record

VenueTransfusion · 2014
Typereview
Languageen
FieldMedicine
TopicMesenchymal stem cell research
Canadian institutionsUniversity of ManitobaCancerCare Manitoba
Fundersnot available
KeywordsMedicineStandardizationBlood productQuality (philosophy)TransplantationCord bloodProduct (mathematics)Intensive care medicineAdverse effectSurgeryOperations managementComputer scienceInternal medicineEngineering

Abstract

fetched live from OpenAlex

Methods of handling, thawing, and infusion of cord blood (CB) products vary substantially among thaw/transplant centers (TCs). This review 1) compares currently available CB product types and thaw methods recommended by CB banks (CBBs), 2) discusses causes of inconsistency in thaw method application at TCs, 3) advises elements to consider in thaw method approval or selection at the TC, 4) provides a procedural template for the traditional thaw methods, and 5) suggests acceptable time from product thaw to infusion and other considerations for safe infusion. It also compares postinfusion adverse reaction and engraftment data as functions of thaw methods. Remarks and suggestions made throughout this review are: 1) not intended to supersede manufacturer's instructions but meant to support the standardization of preparative procedures recommended by CBBs and 2) intended to help TCs to investigate relevant quality issues and handle challenges, especially when the TC is unable to follow recommendations due to foreseeable technical, quality, and/or clinical factors.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.107
GPT teacher head0.464
Teacher spread0.357 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations43
Published2014
Admission routes1
Has abstractyes

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