Willingness of volunteers from Canadian Blood Service's Stem Cell Registry to donate blood, marrow, and other tissues for regenerative therapy
Bibliographic record
Abstract
BACKGROUND: As research surrounding cell-based regenerative therapy advances toward human trials, greater demand for cell products sourced from healthy donors will arise. The extent to which volunteers in Canadian Blood Services Stem Cell Registry would be willing to donate cells to support regenerative therapy is not known and warrants exploration. METHODS: We conducted a Web-based survey to assess factors that would influence donor willingness to donate various tissues (blood, skin, fat, and bone marrow) for regenerative therapy. The survey was provided to 15,000 randomly selected donors who registered between 2013 and 2018. Data from the 1118 respondents were analyzed. RESULTS: Despite a mixed degree of familiarity with regenerative medicine, potential donors were very supportive of donating for direct patient care and for research, and increasing their familiarity by reading a brief paragraph of information on regenerative medicine increased willingness to donate. Canadian Blood Services' stem cell registrants greatly preferred supporting nonprofit groups in research and development in comparison to entities that represent profit-seeking industry involvement. The most important factors influencing donor willingness to donate were having an impact on patients, safety of donation, advancing knowledge in regenerative medicine, a manageable time commitment, and tolerable pain that could be managed. Donors were most willing to donate blood and had mixed responses to donating other tissue types. CONCLUSIONS: Adult volunteers from a national stem cell registry are willing to support donation of biospecimens for regenerative therapy.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".