In the vanguard of biomedicine? The curious and contradictory case of anti‐ageing medicine
Bibliographic record
Abstract
The rise of anti-ageing medicine is emblematic of the current conditions of American biomedicine. Through in-depth interviews with 31 anti-ageing practitioners, we examine how practitioners strive for-and justify-a model of care that runs counter to what they see as the 'assembly line' insurance-managed industry of healthcare. Their motivation, however, is not merely a reaction to conventional medicine. It is derived from what they see as a set of core beliefs about the role of the physician, the nature of the physician-patient relationship, and the function of biomedicine. We analyse this ideology to underscore how anti-ageing medicine is built on a 'technology of the self', a self in need of constant surveillance, intervention, and maintenance. The ultimate goal is to create an optimal self, not just a self free of illness. A fundamental irony is that, despite their self-presentation and the perception of the public, anti-ageing providers do not use practices that are especially 'high-tech' or unconventional. Instead, the management of ageing bodies rests on providers' perceived knowledge of their patients, tailored treatments, and a collaborative pact between the provider and patient.
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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 | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.139 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 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".