Raising awareness may increase the likelihood of hematopoietic stem cell donation: analysis of a nationwide survey using Artificial Intelligence
Bibliographic record
Abstract
Abstract Background: In Italy, the demand for allogeneic transplantation exceeds the number of compatible donors registered in the Italian Bone Marrow Donor Registry (IBMDR). As various factors likely contribute to the donor shortage, our aim was to explore the knowledge, beliefs, opinions, values, and feelings of the Italian population regarding stem cell donation. Methods: An online survey was shared through social media. Two groups of respondents were retrospectively identified as those who were (currently or previously) registered on the IBMDR (Donor Group) and those who had never registered (Non-Donor Group). Statistical analyses were performed to confirm the relationship between respondents’ knowledge level and their willingness to donate. Then, a generative artificial intelligence strategy was applied using questionnaire responses as features to train 6 different classifiers for machine learning process. The aim was to predict the probability of IBMDR enrollment. Results: A total of 1518 respondents from throughout Italy participated in the study. Among NDG, a lower level of knowledge of donation needs (51.7% vs 24.4%, p<0.001) and negative feelings such as fear (Z=-2.2642, p=0.02), perplexity (Z=4.4821, p<0.001), and uncertainty (Z=3.3425, p<0.001) emerged. A higher level of knowledge about stem cell donation and associated processes predicted a greater likelihood of IBMDR enrollment. The machine learning analysis showed an area under the ROC curve (AUC) ranging from 0.65 to 0.81, depending on the classifier. Conclusions: The results underscore the need to improve strategies to raise awareness and knowledge of stem cell donation and its associated process among the Italian population.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".