Bibliographic record
Abstract
Abstract Metastasis is a process by which tumour cells establish new growths at sites in the body distinct from the primary tumour. It is complex, involving a number of stages including escape of tumour cells from the primary tumour and arrest, intravasation, and development of a new growth in a distant organ or lymph node. Although some of the individual stages can be modelled in vitro , an assessment of the ability of individual tumour cells to complete all stages requires the use of in vivo metastasis models. Many different models have been used for studying the multiple aspects of metastasis, but identifying appropriate models that mimic all the characteristics of specific human cancers remains a problem. Metastasis is a very inefficient process with very few tumour cells demonstrating the capacity to grow to form macrometastases, even though they may have initiated the growth of micrometastases. Many specific genetic changes have been identified in tumour cells (and host normal tissue) that can enhance or inhibit metastasis formation but the efficacy of such genetic changes appears to be highly tumour‐cell specific. Recent work developing genetically engineered mouse (GEM) models and models involving human normal tissues transplanted into immune‐deficient mice (SCID‐hu models) is opening up new ways to address some of the many questions remaining about the molecular mechanisms underlying the metastatic process.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".