Bibliographic record
Abstract
Thromboelastography (TEG) is drawing more attention for clinical and in vitro studies of blood coagulation. It can be applied to evaluate the effects of both blood-soluble and insoluble biomaterials on whole blood coagulation from the beginning of coagulation through clot formation to the ending with fibrinolysis. TEG may also identify the relative contributions of various clotting factors, such as fibrinogen and platelets, to the overall coagulation process based on profiles of its variables using whole and partial blood components. A comprehensive review has been conducted on its applications for the assessment of a wide range of blood-contacting biomaterials ranging from polymers to ceramics and biomedical devices involved in many applications. The methodology is different in terms of instrumentation, the methods to activate blood coagulation, the type of blood (citrated versus fresh blood), and study settings (in vitro, in vivo, and clinical trials). The author's own work and future directions are discussed as well. TEG should be considered as one of the most useful tools for evaluating in vitro and in vivo blood-biomaterial interactions for different applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".