Nanomedicine and personalised medicine: understanding the personalisation of health care in the molecular era
Bibliographic record
Abstract
Globally supported by public policy and investment, nanomedicine is presented as an ongoing medical revolution that will radically change the practice of health care from diagnostic to therapeutic, and everything in between. One of nanomedicine's major promises is that of personalised medicine, enabling diagnostics and therapeutics tailored to individual needs and developing a truly 'patient-friendly' medical approach. Based on qualitative interviews with nanomedicine researchers in Canada, this article explores the emerging concept of personalised medicine as it becomes entangled with nanomedical research. More precisely, drawing on insights from science studies and the sociology of expectations, it analyses researchers' perceptions of personalised medicine in the cutting edge of current nanomedicine research. Two perceptions of personalisation are identified; a molecular conception of individuality and a technical conception of personalisation. The article concludes by examining the relationship between the two conceptions and contrasts them with the normative reflex of a more expansive conception of personalised medicine.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.082 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".