Stem cell ‘therapy’ advertisements in China: Infodemic, regulations and recommendations
Bibliographic record
Abstract
During the COVID-19 pandemic, in addition to the pandemic itself, a phenomenon called an 'infodemic'-defined by the World Health Organization as the spread of misleading information on the pandemic-has also gained attention. In the field of stem cell research, researchers and regulators have been fighting against false and misleading information, particularly advertisements for unproven and unauthorized stem cell-based interventions for decades. However, how existing legal and regulatory measures, which vary by country, can be employed to combat such false information is unclear. In this article, we examine the situation in China, where the spread of unauthorized stem cell 'therapies' has drawn patients from not only within China but also from abroad. First, we assess how and to what extent online advertisements promote unproven and unauthorized stem cell-based interventions directly to patients and prospective health consumers in China. Next, we survey the landscape for existing regulatory and administrative measures that may be used to combat false and misleading advertisements in this area. Finally, based on our analysis, we provide three main recommendations that may improve the effectiveness and efficiency of the regulatory measures in curtailing illegitimate advertising of unproven and unauthorized stem cell-based interventions in China. In conclusion, we also call for international collaboration among researchers and regulators in studying and strengthening regulations in this critical area that has so far been neglected in scholarly and policy discussions.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".