Theoretical modelling of fibrinogen supplementation with therapeutic plasma, cryoprecipitate, or fibrinogen concentrate
Bibliographic record
Abstract
BACKGROUND: We aimed to create a theoretical tool to model the effect of three haemostatic agents containing fibrinogen (therapeutic plasma, cryoprecipitate, and fibrinogen concentrate) on the patient's plasma fibrinogen level. METHODS: A mathematical model was developed step-wise. The relationship between the amount of haemostatic agent and plasma fibrinogen level was plotted for each agent. A fibrinogen concentration simulator (FCS(amount)) was developed, where the amount of haemostatic agent was calculated from patient characteristics, agent characteristics, and target plasma fibrinogen level. Refinements were introduced so that (i) FCS(amount) would account for in vivo fibrinogen recovery, (ii) circulatory volume would not increase ad infinitum with increasing amounts, and (iii) red blood cells would be included in the simulation if haematocrit decreased below a certain level. A second FCS (FCS(level)) was created to calculate fibrinogen levels resulting from specified amounts of haemostatic agents. RESULTS: Fibrinogen concentration in haemostatic agents has a critical impact on their ability to increase patients' fibrinogen levels. If the target plasma fibrinogen level approaches the concentration of the fibrinogen source, the required amounts increase exponentially; it is impossible to achieve a target above the concentration of the fibrinogen source. CONCLUSIONS: We successfully developed two theoretical tools answering the questions: 'How much therapeutic plasma, cryoprecipitate, or fibrinogen concentrate would be needed to achieve a specified target fibrinogen level?' and 'What would be the resultant fibrinogen level for a specified amount of haemostatic agent?' The current tools are not intended for clinical application, but they are potentially useful for educational purposes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".