Opportunities for Translational Epidemiology: The Important Role of Observational Studies to Advance Precision Oncology
Bibliographic record
Abstract
Within current oncology practice, several genomic applications are being used to inform treatment decisions with molecularly targeted therapies in breast, lung, colorectal, melanoma, and other cancers. This commentary introduces a conceptual framework connecting the full spectrum of biomedical research disciplines, including fundamental laboratory research, clinical trials, and observational studies in the translation of genomic applications into clinical practice. The conceptual framework illustrates the contribution that well-designed observational epidemiologic studies provide to the successful translation of these applications, and characterizes the role observational epidemiology plays in driving the dynamic and iterative bench-to-bedside, and bedside-to-bench translation continuum. We also discuss how the principles of this conceptual model, emphasizing integration of multidisciplinary research, can be applied to the evolving paradigm in "precision oncology" focusing on multiplex tumor sequencing, and we identify opportunities for observational studies to contribute to the successful and efficient translation of this paradigm.Cancer Epidemiol Biomarkers Prev; 24(3); 484-9. ©2015 AACR.
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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 | 0.394 | 0.443 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.013 | 0.025 |
| Insufficient payload (model declined to judge) | 0.004 | 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".