Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.019 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it