Applying Implementation Science in the Field of Transplant and Cellular Therapy
Bibliographic record
Abstract
Implementation science (IS) is a systematic way to approach the broader adoption of evidence-based practices and has as its goal to understand and address the gap between research and practice, ensuring that research findings are effectively translated into practice and policy to improve health outcomes and service. We describe the various facets of IS and their relevance to the field of hematopoietic cell transplantation and cellular therapy (HCT/CT) with an emphasis on health equity, community engagement, and systems approach. We also review the similarities and differences among clinical research, quality improvement, and IS. Additionally, we describe how the Center for International Blood and Marrow Transplant Research applies IS across various phases: dissemination, analyzing current practices, and developing implementation intervention strategies. This includes designing studies and evaluations, scaling up operations, and ensuring sustainability. Lastly, we discuss further applications of IS in HCT/CT including the application to prospective research studies, collaboration across the field, and standardization and adoption of best practices. The application of IS in HCT/CT is pivotal to bringing research benefits directly to all patients. Through partnership, open-mindedness, and a commitment to evidence-based practice, we can collectively ensure the greatest impact of research on improving patient outcomes following HCT/CT.
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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 | 0.021 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".