Clinical use of autologous cell-based therapies in an evolving regulatory landscape: A survey of patient experiences and perceptions
Bibliographic record
Abstract
<ns3:p> <ns3:bold>Background</ns3:bold> : Clinical treatments involving autologous cell-based therapies (ACBT) remain prevalent despite a lack of scientific backing and an evolving regulatory landscape aimed at assessing their safety and efficacy for clinical adoption. This study seeks to assess patients’ experiences and perceptions of clinical treatments involving ACBT and their knowledge and views of the regulatory context and associated governance issues. <ns3:bold>Methods</ns3:bold> : An anonymous online survey of 181 participants who have been treated or are in the process of being treated with ACBT was conducted. Recruitment was via social media platforms. Data was collected through Qualtrics and analyzed using SPSS 29 for the quantitative responses and NVivo 1.7.1 for the qualitative responses. <ns3:bold>Results</ns3:bold> : Several themes emerged from the data, including the prominent role of healthcare providers throughout the patient journey, informational practices during the clinical encounter, the high prevalence of pay-for-participation trials, patients’ gaps in regulatory knowledge, and patients’ priorities regarding clinical trials and regulation of ACBT. <ns3:bold>Conclusions</ns3:bold> : The study makes a novel contribution to the literature by providing the first analysis of patients’ experiences and perceptions of an emerging cell-based therapy within an evolving regulatory landscape. The findings serve as a valuable resource for developing policy, promoting scientific rigor, and ensuring ethical oversight of ACBT and other upcoming cell-based therapies. </ns3:p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".