Referee report. For: Clinical use of autologous cell-based therapies in an evolving regulatory landscape: A survey of patient experiences and perceptions [version 2; peer review: 2 approved]
Bibliographic record
Abstract
Background : 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. Methods : 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. Results : 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. Conclusions : 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.259 | 0.081 |
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".