Genomic Medicine: New Frontiers and New Challenges
Bibliographic record
Abstract
BACKGROUND: The practice of personalized medicine has made large strides since the introduction of high-throughput technologies and the vast improvements in computational biotechnology. The personalized-medicine approach to cancer management holds promise for earlier disease detection, accurate prediction of prognosis, and better treatment options; however, the early experience with personalized medicine has revealed important concerns that need to be addressed before research findings can be translated to the bedside. CONTENT: We discuss several emerging "practical" or "focused" applications of personalized medicine. Molecular testing can have an important positive impact on health and disease management in a number of ways, and the list of specific applications is evolving. This list includes improvements in risk assessment, disease prevention, identification of new disease-related mutations, accurate disease classification based on molecular signatures, selection of patients for enrollment in clinical trials, and development of new targeted therapies, especially for metastatic tumors that are refractory to treatment. Several challenges remain to be addressed before genomics information can be applied successfully in the routine clinical management of cancers. Further improvements and investigations are needed in data interpretation, extraction of actionable items, cost-effectiveness, how to account for patient heterogeneity and ethnic variation, and how to handle the risk of "incidental findings" in genetic testing. SUMMARY: It is now clear that personalized medicine will not immediately provide a permanent solution for patient management and that further refinement in the applications of personalized medicine will be needed to address and focus on specific issues.
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.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".