Personalized medicine and genome-based treatments: Why personalized medicine ≠ individualized treatments
Bibliographic record
Abstract
The sequencing of the human genome and decreasing costs of sequencing technology have led to the notion of ‘personalized medicine’. This has been taken by some authors to indicate that personalized medicine will provide individualized treatments solely based on one’s DNA sequence. We argue this is overly optimistic and misconstrues the notion of personalization. Such interpretations fail to account for economic, policy and structural constraints on the delivery of healthcare. Furthermore, notions of individualization based on genomic data potentially take us down the road of genetic reductionism obscuring the role of environmental factors in disease and ill health. We propose that one should see personalized medicine as a way of using personal genomic information to stratify individuals into subpopulations and suggest that personalized medicine be seen within a broader idea of personalized healthcare, reflecting healthcare that integrates personal genomic data into cultural, environmental and personal contexts.
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 | 0.029 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.087 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.016 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 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".