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
Cancer has long been viewed as a heterogeneous population of cells. Cancerous cells may often originate from the transformation of normal stem cells, similar signaling pathways may regulate self-renewal in stem cells and cancer cells. The concept that cancer might arise from a rare population of cells with stem cell properties was proposed for more than a century. Cancer stem cell hypothesis has began to be accepted recently due to the advances in stem cell biology and the development of new animal models to measure self-renewal that drive tumorigenesis. Cancer stem cells have been identified and purified in a variety of tumors (blood, breast, brain, colon, lung, pancreas) using unique stem cell markers such as CD44, CD133 and aldehyde dehydrogenases. Cancer stem cell gene signatures have been examined. This review will discuss the evolution of cancer stem cell research and summarize the recent patents related to the cancer stem cell markers, the methods to detect and modulate cancer stem cells and cancer stem cell-targeted treatment. With the advances in cancer stem cell research, the new patent applications, particularly the new drugs on cancer stem cells treatments are expected to be increasing.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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