Progress in Chemoprevention Drug Development: The Promise of Molecular Biomarkers for Prevention of Intraepithelial Neoplasia and Cancer—A Plan to Move Forward
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article reviews progress in chemopreventive drug development, especially data and concepts that are new since the 2002 AACR report on treatment and prevention of intraepithelial neoplasia. Molecular biomarker expressions involved in mechanisms of carcinogenesis and genetic progression models of intraepithelial neoplasia are discussed and analyzed for how they can inform mechanism-based, molecularly targeted drug development as well as risk stratification, cohort selection, and end-point selection for clinical trials. We outline the concept of augmenting the risk, mechanistic, and disease data from histopathologic intraepithelial neoplasia assessments with molecular biomarker data. Updates of work in 10 clinical target organ sites include new data on molecular progression, significant completed trials, new agents of interest, and promising directions for future clinical studies. This overview concludes with strategies for accelerating chemopreventive drug development, such as integrating the best science into chemopreventive strategies and regulatory policy, providing incentives for industry to accelerate preventive drugs, fostering multisector cooperation in sharing clinical samples and data, and creating public-private partnerships to foster new regulatory policies and public education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it