Toward responsible longevity medicine: Swiss framework for healthy longevity medicine clinics
Bibliographic record
Abstract
Introduction Rapid advances in geroscience have catalyzed a proliferation of private longevity clinics while regulatory oversight lags. Switzerland is central to this landscape yet lacks a dedicated framework. We propose a voluntary, hospital-grade framework integrating leadership, evidence generation, cybersecurity, and patient safety—to guide responsible growth.Methods We performed a structured search of PubMed, Embase, and Web of Science (January 2000–March 2025). Eligible human studies and regulatory texts were appraised with GRADE; when multiple sources existed, we prioritized the most recent meta-analyses or largest randomized trials.Results We codify the patient journey into a tiered pathway—risk stratification, multimodal diagnostics, and therapeutic escalation—anchored in extended informed consent, equity safeguards, and public outcome dashboards. Diagnostics (frailty indices to multi-omic clocks) are ranked by analytic validity and clinical utility. Therapeutics (e.g., senotherapeutics, caloric-restriction mimetics, rapalogs) are mapped to evidence tiers and contexts of use. Evidence gaps include pragmatic trials of senotherapeutics, economic validation of composite aging clocks, and ICD-11 recognition of pathological aging.Conclusions This Swiss-aligned framework couples evidence-weighted interventions with system-level transparency. Its adoption could extend healthspan while maintaining scientific rigor, patient safety, and distributive justice across diverse care settings. Implementation should include public dashboards, external audits, and prospectively registry-embedded advanced protocols.
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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 | 0.172 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".