Bibliographic record
Abstract
One in 9 women will develop breast cancer in their lifetime, and 1 in 29 will die from the growth and spread of this disease, with equally poor outcomes in susceptible men. Breast cancer arises from the interactions of many risk factors, such as changes to normal gene expression and function, and environmental exposures to cancer‐causing chemicals or high fat (HF) diets. Our objectives are to predict who will develop aggressive tumours, and more importantly, develop prevention strategies. Peroxisome proliferator‐activated receptor (PPAR)γ is a protein that plays a role in many cancers, and controls the normal expression of genes needed for fat and sugar metabolism. Our previous work using PPARγ haploinsufficient mice suggests turning on PPARγ stops chemical‐induced breast tumour progression. Since breast tissue is composed of different PPARγ expressing cell types, each with unique signal patterns, defining those essential to stop breast tumour progression will help move our work into the clinic. We used our established models to definitively show how mammary stromal PPARγ stops breast tumour progression, and provide further support for the use of PPARγ activators as a novel chemotherapeutic to improve the quality of life for breast cancer patients. In the longterm, we expect these studies will be the basis to prevent deaths among the ~259,000 American and Canadian breast cancer patients diagnosed in 2013.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.010 | 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 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".