Between personalized and racialized precision medicine: A relative resources perspective
Bibliographic record
Abstract
Since the completion of the first human genome sequencing project in 2003, the potential for incorporating genomics into clinical practice in the pursuit of precision medicine has garnered a great amount of attention and interest. Existing literature presents a dichotomous view of the future of medicine: either precision medicine will replace race-based medicine or race-based medicine will persist despite developments in genetic research and genomic medicine. Drawing on interview data with 46 physicians and scientists in the USA, Canada, and Singapore who are conducting research or practicing precision medicine in the context of cancer treatment and prevention, this article attempts to contribute to the existing debate by proposing a ‘relative resources’ perspective to explain which approach will dominate in a particular healthcare setting. The author elaborates on the ‘heterogeneity of resources’ and suggests that the extent to which precision medicine will be personalized or racialized/ethnicized in the clinic will most likely be a function of the relative availability of resources – including but not limited to financial, human and computer informatics, and legal and infrastructural resources – at individual and collective levels in healthcare 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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.048 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".