The ethical framing of personalized medicine
Bibliographic record
Abstract
PURPOSE OF REVIEW: Personalized medicine encompasses the use of biological information such as genomics to provide tailored interventions for patients. The review explores the ethical, legal, and social issues that have emerged with personalized medicine and must be considered because of the complex nature of providing individualized care within a clinical setting. RECENT FINDINGS: Recent studies found that the use of personalized medicine presents challenges in multiple areas: biobanking and informed consent, confidentiality, genetic discrimination, return of results, access to treatment, clinical translation, direct-to-consumer genetic testing, emerging duties, and knowledge mobilization. SUMMARY: Although personalized medicine provides benefits in treating patients in a manner that is more suited to their genetic profile, there are challenges that must be discussed to ensure the protection and fair treatment of individuals. The issues concerning personalized medicine are widespread, and range from individual privacy to the stratification and discrimination of sub-populations based on ethnicity. These issues have considerable impact on the individual and society. A thorough exploration of these ethical issues may identify novel challenges as well as potential avenues for resolution.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".