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Record W2111224997 · doi:10.1111/trf.13169

Routine filtration of hematopoietic stem cell products: the time has arrived

2015· article· en· W2111224997 on OpenAlexaff
Kristjan Paulson, Scott Gilpin, Tanner A. Shpiruk, Karla Anjos, Marie Tulloch, Angleine Giftakis, Anna R. Blankstein, David Szwajcer, Donna A. Wall

Bibliographic record

VenueTransfusion · 2015
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsWestern UniversityUniversity of ManitobaCancerCare Manitoba
Fundersnot available
KeywordsFiltration (mathematics)Stem cellHematopoietic stem cellHaematopoiesisPotencyMedicineAndrologyChemistryBiologyCell biologyBiochemistry

Abstract

fetched live from OpenAlex

BACKGROUND: Most blood products are infused at the time of transfusion through a standard blood filter, designed to capture macroaggregates and cellular debris that might be harmful to the patient if infused. Hematopoietic stem cell products are not universally filtered, likely due to concern about loss of viable stem cells in the filtration process. STUDY DESIGN AND METHODS: We conducted a two-phase study to better understand the safety of routine filtration. First, surplus cryopreserved stem cell products were thawed and filtered, with markers of viability and potency measured. Second, routine filtration was implemented as part of routine practice at our center, and date of neutrophil and platelet (PLT) recovery was compared to historical controls. RESULTS: In the first phase, there was no difference seen in any markers of viability or potency for products after routine filtration. Based on those results, routine filtration was implemented. There was no difference in neutrophil or PLT engraftment. Thus, in this study, routine filtration did not impact the number of viable stem cells and did not delay engraftment. CONCLUSION: Given the very real harm posed by infusion of macroaggregates and cellular debris, and no clear disadvantage to filtration, routine filtration of stem cell products should be considered the standard of care.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.036
GPT teacher head0.246
Teacher spread0.210 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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