The contribution of platelets in the production of cryoprecipitates for use in a fibrin glue
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Cryoprecipitate has a wide application for use as a fibrin glue. In some situations, platelets are added to the preparation in order to enhance the fibrin glue. MATERIALS AND METHODS: Fresh plasma was collected by apheresis from the same donor to produce 250 ml of platelet-rich plasma (PRP) or platelet-poor plasma (PPP) (n = 12 each). Cryoprecipitate was then produced following the standards of the American Association of Blood Banks and resuspended to a total volume of 8 ml, from which aliquots were removed and assayed. Clot formation was measured using the thromboelastogram. RESULTS: The protein content of the two preparations was identical for PRP and PPP. Results for fibrinogen (PPP 475 +/- 220 mg; PRP 399 +/- 215 mg), Factor VIII (PPP 186 +/- 67 IU; PRP 175 +/- 70 IU) and von Willebrand Factor (PPP 260 +/- 104 IU; PRP 221 +/- 88 IU) were not significantly different. The concentration of platelet-derived growth factor was markedly higher (a 100-fold increase at 3778 +/- 1036 ng) when platelets were added to the plasma. There was a small, but not statistically significant, difference in the rate of clot formation (R = 2.3 for PPP and 3.8 for PRP) and clot strength (MA = 63.4 for PPP and 56.6 for PRP) between PPP and PRP cryoprecipitates when measured using the thromboelastogram. CONCLUSIONS: Platelets do not significantly increase the concentration of the usual constituents of cryoprecipitate; however, the levels of platelet-derived growth factor are markedly enhanced. Therefore, there are advantages for using PRP to enhance the growth of new tissue.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".