Features Associated with Survival Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Editor's Summary
Bibliographic record
Abstract
new biological aspects of cancer tissue. brain with an unbiased image processing system can extract more information from microcopy images and discover humantended to have more inflammatory cells in the stroma (picked up as dark areas by the software). Replacing the epithelial cells infiltrating the stroma, which resulted in high-risk stromal matrix variability scores. These patients also cancer itself but were from the adjacent stromal tissue. Women with worse outcomes tended to have thin cords of An unexpected finding was that the features that were the best predictors of patient survival were not from the with overall survival. status. In another, completely independent group of women from Vancouver, the C-Path score was also associated other measures of cancer severity including pathology grade, estrogen receptor status, tumor size, and lymph node samples so it could learn the difference. The C-Path score yielded information above and beyond that from many hand-markedpart of cancer diagnosis, took a bit of extra work: The authors needed to provide the software with some among the very large set of measurements of the image. Classifying the tissue as epithelial or stromal, an important predefined by a pathologist as being relevant to cancer; instead, the software itself found the cancer-related features samples from patients who had died sooner. The key aspect of this analysis was that these features were not from patients in the Netherlands. From more than 6000 features, the software found a set that were associated with
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".