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
Platelets play a crucial role in the pathophysiological processes of hemostasis and thrombosis. Increasing evidence indicates that they fulfill much broader roles in balancing health and disease. The presence of tumor cells affects platelets both numerically, through a wide variety of mediators and cytokines, or functionally through tumor cell-induced platelet activation, the first step toward cancer-induced thrombosis. This induction results from signaling events through the different platelet receptors, or may be cytokine-mediated. Reciprocally, upon activation, the platelets will release a myriad of growth factors from their dense and α-granules and peroxisomes; these will directly impact tumor growth, tethering, and spread. A similar cross-talk is initiated between tumor microvesicles stimulating the platelets and platelet microparticles, promoting both thrombosis and tumor growth. A vicious loop of activation thereafter takes place. Platelets directly and indirectly promote tumor growth, and enable a molecular mimicry coating the malignant growth and allowing metastasizing cells to escape T-cell-mediated immunity and natural killer cell surveillance. Breaking this vicious activation loop with nonspecific platelet inhibitors, such as aspirin, or by targeting specific sites on the activation cascade may offer a mean to reduce both the risks of development and progression of cancer and the risk of thrombosis.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it