Bibliographic record
Abstract
Introduction Platelet aggregation is involved in the formation of hemostatic plugs and arterial thrombi. Under normal circumstances, platelets are non-adhesive and circulate singly, but following vessel wall injury they adhere to the injury site and to each other. During the hemostatic process, platelet aggregates, stabilized by fibrin, arrest bleeding from injured or severed vessels. In contrast to this useful function, platelet aggregates that form on injured vessels, on ruptured atherosclerotic plaques, or in regions of high shear contribute to the narrowing of blood vessels. If thrombi are unstable, they may embolize and block smaller vessels downstream from an injury site. Since platelet aggregation has a major role in the clinical complications of atherosclerosis (myocardial infarction, ischemic stroke, and peripheral vascular disease), there is intensive study of the processes involved in platelet aggregation and of inhibitors of platelet activation. In vivo, activators of platelets include the agonists that are listed in Table 23.1. Receptors for some of the most important agonists are discussed in Chapters 8–11. Aggregating agents can act singly, and are frequently studied singly in vitro, but in vivo they undoubtedly act in concert with each other in a process described as synergism. Synergistic responses result in a combined effect that is greater than the additive effects of the single stimuli. In vivo, the most important aggregating agents are collagen in the vessel wall, ADP from red blood cells or released from the platelets themselves, thromboxane A 2 formed by stimulated platelets, and thrombin, although other agonists such as serotonin may contribute to the aggregation process.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.028 |
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".