Cryoprecipitate production: the use of additives to enhance the yield
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".