Impact of BMI and PRP Platelet and Red Blood Cell Content on the Coagulation Kinetics of Ortho-R/PRP Mixtures
Bibliographic record
Abstract
Ortho-R (ChitogenX Inc., Kirkland, QC, Canada) is a lyophilized chitosan formulation that also contains calcium chloride and trehalose. Ortho-R was designed to be solubilized in autologous platelet-rich plasma (PRP), a blood-derived component, in order to become an injectable implant that augments the surgical repair of soft tissues. The Ortho-R/PRP formulation coagulates post-application, similarly to blood. Having the ability to predict the speed of coagulation of an Ortho-R/PRP mixture prepared with PRP isolated from a specific patient would be an advantage in the operating room. The purpose of this study was to investigate whether human donor characteristics (age, sex, body mass index, habits) and autologous PRP properties would have an impact on Ortho-R/PRP mixture coagulation. Clot maximal amplitude and shear elastic modulus were significantly positively correlated with body mass index and platelet concentration in the isolated PRPs. Clot formation time, maximal amplitude and shear elastic modulus were all negatively correlated with PRP red blood cell concentration (and associated hemoglobin and hematocrit content). Donor characteristics were not good predictors of coagulation kinetics in Ortho-R/PRP mixtures. Some of the isolated PRP properties were better predictors of Ortho-R/PRP coagulation kinetics. However, predicting how an Ortho-R/PRP mixture from a particular patient will coagulate is very difficult since all PRP isolation devices yield heterogeneous PRPs and analysis of the isolated PRPs occurs post-administration.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".