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 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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".