Is belief larger than fact: expectations, optimism and reality for translational stem cell research
Bibliographic record
Abstract
BACKGROUND: Stem cell (SC) therapies hold remarkable promise for many diseases, but there is a significant gulf between public expectations and the reality of progress toward clinical application. Public expectations are fueled by stakeholder arguments for research and public funding, coupled with intense media coverage in an ethically charged arena. We examine media representations in light of the expanding global landscape of SC clinical trials, asking what patients may realistically expect by way of timelines for the therapeutic and curative potential of regenerative medicine? METHODS: We built 2 international datasets: (1) 3,404 clinical trials (CT) containing 'stem cell*' from ClinicalTrials.gov and the World Health Organization's International Clinical Trials Registry Search Portal; and (2) 13,249 newspaper articles on SC therapies using Factiva.com. We compared word frequencies between the CT descriptions and full-text newspaper articles for the number containing terms for SC type and diseases/conditions. We also developed inclusion and exclusion criteria to identify novel SC CTs, mainly regenerative medicine applications. RESULTS: Newspaper articles focused on human embryonic SCs and neurological conditions with significant coverage as well of cardiovascular disease and diabetes. In contrast, CTs used primarily hematopoietic SCs, with an increase in CTs using mesenchymal SCs since 2007. The latter dominated our novel classification for CTs, most of which are in phases I and II. From the perspective of the public, expecting therapies for neurological conditions, there is limited activity in what may be considered novel applications of SC therapies. CONCLUSIONS: Given the research, regulatory, and commercialization hurdles to the clinical translation of SC research, it seems likely that patients and political supporters will become disappointed and disillusioned. In this environment, proponents need to make a concerted effort to temper claims. Even though the field is highly promising, it lacks significant private investment and is largely reliant on public support, requiring a more honest acknowledgement of the expected therapeutic benefits and the timelines to achieving them.
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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 | 0.104 | 0.234 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.022 | 0.025 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".