An Innovative, Comprehensive Mapping and Multiscale Analysis of Registered Trials for Stem Cell-Based Regenerative Medicine
Bibliographic record
Abstract
UNLABELLED: We aim to provide an innovative, comprehensive way of mapping the profusion of stem cell-based clinical trials registered at ClinicalTrials.gov to explore the diversity of the fields of application and the temporal complexity of the domain. We used a chord diagram and phylogenetic-like tree visualizations to assist in data mining and knowledge discovery. The search strategy used the following terms: "stromal OR stem OR mesenchymal OR progenitor." The Medical Subject Headings (MeSH) thesaurus was used to more finely classify diseases treated by stem cells, from large fields of application to specific diseases. Of the 5,788 trials screened, 939 were included, 51.1% of which were related to mesenchymal stem cells (MSCs). No real specificity emerged as to the therapeutic uses of the different types of stem cells. More than half the MSC studies concerned allogeneic MSCs and received more support from industry than autologous MSC studies (p < .001). Over time, the uses of cultured cells have increased greatly, particularly since 2009. Cells derived from adipose tissue are also increasingly used in trials compared with bone marrow cells. The use of adipose-derived stromal cells was predominantly autologous (p < .001), restricted to European countries (p < .01), and supported by industry (p = .02) compared with other MSCs. Details about MeSH keywords are available at http://multireview.perso.sfr.fr/. In conclusion, mapping may reveal a lack of global strategy despite the regulations and the related costs associated with good manufacturing practices. A systematic approach to preclinical data, intended to objectively and robustly reveal the most appropriate fields with the most efficient cells, is needed. Repeated exchanges between the bench and the bedside are necessary. SIGNIFICANCE: Except for a few trials concerning specific tissue stem cells used in their corresponding tissues, this global analysis revealed no real specificity of stem cell uses (including mesenchymal stromal cells). This raised the question of the physiopathological rationale for these uses and the lack of a global strategy despite the regulations and the related costs associated with good manufacturing practices. This original method, leading to the development of new concepts from already available data, would help policymakers to optimize resources and investments in terms of public health priorities. Such an approach should draw parallels between in vitro, in vivo, and human data. Exchanges in both directions between preclinical and clinical research could optimize the parameters of clinical trials step by step.
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 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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".