Abstract A034: Model-free scoring system for risk prediction and early detection
Bibliographic record
Abstract
Abstract There is a growing need to develop risk-prediction scoring systems for survival data that can facilitate early detection and guide clinical decision-making for early-onset cancers. Identifying important risk factors (e.g., genetic or molecular biomarkers) is particularly critical for patient screening and timely treatment recommendations in this population. However, most existing methods either rely on strong modeling assumptions (such as proportional hazards) or are restricted to binary outcomes, limiting their utility in this context. In this article, we propose a flexible approach that simultaneously selects important risk factors and constructs an optimal linear risk score by maximizing a pseudo-likelihood function based on the time-dependent area under the receiver operating characteristic curve. This framework is especially relevant for evaluating risk profiles and recommending personalized surveillance or early interventions for patients at elevated risk of early-onset cancers. We establish desirable theoretical properties of the method, including asymptotic normality and the oracle property after variable selection. Its performance is further assessed through simulation studies and illustrated with an application to hepatocellular carcinoma data, where the method is used to identify biomarkers predictive of early-stage disease and to support strategies for early detection and intervention. Citation Format: Ying Yuan. Model-free scoring system for risk prediction and early detection [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr A034.
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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 | 0.006 | 0.017 |
| 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) | 0.000 | 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".