Abstract IA017: Molecular classification and stratification: Diving deeper
Bibliographic record
Abstract
Abstract As we move forward with integration of molecular classification into clinical care the assessment of additional clinicopathological and molecular features in the context of molecular subtype will enable us to further refine prognosis. In addition, molecular classification can enable stratification of clinical cohorts that have been often thought of as homogeneous but are clearly more diverse than recognized. These last few years have produced an abundance of literature supporting not only the value of consistent categorization and prognostic value but also predictive implications enabling action/change in practice. Molecular classification-driven care in endometrial cancer will reduce variation in practice with improved knowledge translation critical to implementation and uptake. Citation Format: Jessica N. McAlpine. Molecular classification and stratification: Diving deeper [abstract]. In: Proceedings of the AACR Virtual Special Conference: Endometrial Cancer: New Biology Driving Research and Treatment; 2020 Nov 9-10. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(3_Suppl):Abstract nr IA017.
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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 | 0.045 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.015 |
| Insufficient payload (model declined to judge) | 0.021 | 0.011 |
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".