International Society for Cell & Gene Therapy Position Paper: Key considerations to support evidence-based cell and gene therapies and oppose marketing of unproven products
Bibliographic record
Abstract
The field of regenerative medicine, including cellular immunotherapies, is on a remarkable growth trajectory. Dozens of cell-, tissue- and gene-based products have received marketing authorization worldwide while hundreds-to-thousands are either in preclinical development or under clinical investigation in phased clinical trials. However, the promise of regenerative therapies has also given rise to a global industry of direct-to-consumer offerings of prematurely commercialized cell and cell-based products with unknown safety and efficacy profiles. Since its inception, the International Society for Cell & Gene Therapy Committee on the Ethics of Cell and Gene Therapy has opposed the premature commercialization of unproven cell- and gene-based interventions and supported the development of evidence-based advanced therapy products. In the present Guide, targeted at International Society for Cell & Gene Therapy members, we analyze this industry, focusing in particular on distinctive features of unproven cell and cell-based products and the use of tokens of scientific legitimacy as persuasive marketing devices. We also provide an overview of reporting mechanisms for patients who believe they have been harmed by administration of unapproved and unproven products and suggest practical strategies to address the direct-to-consumer marketing of such products. Development of this Guide epitomizes our continued support for the ethical and rigorous development of cell and cell-based products with patient safety and therapeutic benefit as guiding principles.
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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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.028 | 0.020 |
| Insufficient payload (model declined to judge) | 0.025 | 0.034 |
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".