Bibliographic record
Abstract
Tissue factor (TF), the key regulator of haemostasis and angiogenesis, is also involved in the pathology of several diseases, including cardiovascular, inflammatory and neoplastic conditions. In the latter, TF is upregulated by cancer cells, as well as by certain host cells, and it is the interactions between these distinct pools of TF-expressing cells that likely influence tumour progression in several ways. Furthermore, the release of TF microparticles into the circulation is thought to contribute to the systemic coagulopathies commonly observed in cancer patients. The direct regulation of TF by oncogenic events has provided a plausible explanation for the relatively common overexpression of TF in various cancers and its involvement in tumour growth, angiogenesis, metastasis and coagulopathy. However, this constitutive influence is modified by the tumour microenvironment, cellular interactions and host factors rendering TF expression patterns complex and heterogeneous. It appears that in many biological contexts TF plays a central role in disease progression and thereby potentially constitutes an attractive therapeutic target, especially in scenarios where the risk of bleeding can be avoided by selecting appropriate medications, refined dosing or by targeting the signalling component of TF activity. The efficacy and safety of such approaches still awaits clinical verification.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".