Toward an international standardisation roadmap for nanomedicine
Bibliographic record
Abstract
The French National Metrology Institute (LNE) initiated a series of events to identify priorities for test methods and their harmonisation that directly address regulatory needs in Nanomedicine. One of these workshops entitled "The International Standardisation Roadmap for Nanomedicine" held in October 2023 (Paris, France) brought together key experts in the characterisation of nanomedicines and medical products containing nanomaterials, including the Joint Research Centre of the European Commission, SINTEF Industry and the metrology institutes of France, the UK, the USA and Canada, two flagship initiatives of the European Commission (PHOENIX and SAFE-n-MEDTECH Open Innovation Test Beds), representatives of a working party on mRNA vaccines at the European Directorate for the Quality of Medicines (EDQM) and members of international standardisation and pre-normative organisations (including CEN, ISO, ASTM, VAMAS). Two take-home message came out from the discussion. First, developing standard test methods and Reference Materials (RMs) for nanomedicines is a key priority for the European Commission and various stakeholders. Furthermore, there was a unanimous recognition of the need for a unified approach between standardisation committees, regulators and the nanomedicine community. At the USA, Canadian and European level, examples of success stories and of future initiative have been discussed. Future perspectives include the creation of a dedicated Working Group under CEN/TC 352 to consolidate efforts and develop a nanomedicine standardisation roadmap.
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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 | 0.130 | 0.072 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".