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Cryoprecipitate production: the use of additives to enhance the yield

2006· article· en· W2146720852 on OpenAlexaff
Hanan M. Yousef, Doris Neurath, Mark S. Freedman, G. Rock

Bibliographic record

VenueClinical & Laboratory Haematology · 2006
Typearticle
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsCryoprecipitateFibrinogenSodium citrateChemistryPartial thromboplastin timeYield (engineering)ChromatographyCoagulationInternal medicineBiochemistryMedicine

Abstract

fetched live from OpenAlex

Cryoprecipitate is still widely used to treat hemophilia A in developing countries. However, the yield of factor VIII is relatively low averaging, i.e. only 50%. We have attempted to enhance the yield by adding sodium citrate to the plasma following the method of Shanbrom and Owens (Blood 98, 2001, 60a). Fresh-frozen plasma (FFP) units were processed either as control plasma or after the addition of 10% sodium citrate. Cryoprecipitate was produced from both. After resuspension, calcium chloride was added to the citrated cryoprecipitate to correct for excess citrate prior to testing. The levels of FVIII and fibrinogen were determined in both preparations. The citrated cryoprecipitates had varying yields of fibrinogen and FVIII in the cryoprecipitate. The FVIII levels varied from 34% to 215% recovery. Fibrinogen ranged from 55.5% to 121.4%. We found that the addition of increasing amounts of CaCl2 to normal plasma raised the FVIII values from 1.0 to 4 U/ml. To determine the possibility of assay influence we added different quantities of CaCl2 to control plasma and measured the FVIII and activated partial thromboplastin time levels. Addition of citrate to plasma resulted in an increased total amount of cryoprecipitate much of which was citrate. Assays showed considerable ranges in the quantity of FVIII and fibrinogen. Activation of FVIII can be caused by addition of excess calcium.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.114
GPT teacher head0.421
Teacher spread0.306 · 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 designNot applicable
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
Published2006
Admission routes1
Has abstractyes

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