How do we leverage implementation science to support and accelerate uptake of clinical practice guidelines in transfusion medicine
Bibliographic record
Abstract
BACKGROUND: Developing and disseminating clinical practice guidelines is a common strategy used to inform practice and address evidence-to-practice gaps that are prominent in transfusion medicine. Despite a highly systematic method for synthesizing evidence into guideline recommendations, comparatively little attention is paid to the real-world implementation of the recommendations in routine practice. A more scientific approach drawing on learnings from the field of implementation science is therefore warranted. STUDY DESIGN AND METHODS: In this article, we propose a methodological roadmap to embed implementation science principles, frameworks, and methods to facilitate the development and uptake of transfusion medicine guidelines. We draw upon research undertaken in partnership with the International Collaboration of Transfusion Medicine Guidelines (ICTMG) to illustrate the roadmap in action. RESULTS: The methodological roadmap constitutes five steps which have been matched to existing processes for developing and implementing clinical practice guidelines: (1) environmental scan; (2) detailing who needs to do what differently, per guideline recommendation; (3) barriers and enablers assessment; (4) tailoring implementation strategies to identified barriers and enablers; and (5) implementation and evaluation of implementation strategies. For each step, we define the key concepts and methods involved, and share examples from work done with ICTMG to support transfusion medicine guideline implementation. DISCUSSION: We intend this methodological roadmap for clinicians, researchers, and organizations involved in supporting clinical practice guideline use. Informed by principles, frameworks, and methods from implementation science, the roadmap can provide a more structured, transparent, and replicable approach to improve the implementation of guideline recommendations in transfusion medicine.
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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 | 0.016 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".