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
gene encodes KISS1, a protein that is rapidly processed in serum into smaller but biologically active peptides called kisspeptins (KPs). KISS1 and the KPs signal via the G-protein coupled receptor KISS1R. While KISS1 and KPs are recognized as potent positive regulators of the reproductive neuroendocrine axis in mammals, the first reported role for KISS1 was that of metastasis suppression in melanoma. Since then, it has become apparent that KISS1, KPs, and KISS1R regulate the development and progression of several cancers but interestingly, while these molecules act as suppressors of tumorigenesis and metastasis in many cancers, in breast and liver cancer they function as promoters. Thus, they join a small but growing number of molecules that exhibit dual roles in cancer highlighting the importance of studying cancer in context. Given their roles, KISS1, KPs and KISS1R represent important molecules in the development of novel therapies and/or as prognostic markers in treating cancer. However, getting to that point requires a detailed understanding of the relationship between these molecules and different cancers. The purpose of this review is therefore to highlight and discuss the clinical studies that have begun describing this relationship in varying cancer types including breast, liver, pancreatic, colorectal, bladder, and ovarian. An emerging theme from the reviewed studies is that the relationship between these molecules and a given cancer is complex and affected by many factors such as the micro-environment and steroid receptor status of the cancer cell. Our review and discussion of these important clinical studies should serve as a valuable resource in the successful development of future clinical studies.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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