Comparing continuous and discrete analyses of breast cancer survival information
Bibliographic record
Abstract
Treatment of cancer is becoming increasingly personalized and biomarkers continue to be developed to refine treatment decisions. Tumour mRNA abundance data is commonly used to develop such biomarkers, often to predict patient survival. However, survival analyses present unique challenges and it is unknown whether analysing mRNA abundance information in a discrete or continuous manner yields different results. To address this, we analysed 1988 primary breast tumour transcriptomes. When compared univariately, approximately 60% of all genes showed differences between the discrete and continuous Cox proportional hazards models with q-value differences spanning four orders of magnitude for some genes. Further, hybrid models using both continuous and discrete data used to classify poor prognosis via random forest outperformed models using a single type of information. Thus some genes appear to continuously contribute to poor prognosis while others display threshold effects, and incorporating this into biomarker development is a key unexplored avenue.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".