Improving Patient Blood Management Programs: An Implementation Science Approach
Bibliographic record
Abstract
Organized patient blood management (PBM) programs function in numerous hospitals and health systems around the world contributing to improved patient outcomes as well as increased patient engagement, decreased resource use, and reductions in health care costs. PBM "programming" ranges from the implementation of single strategies/initiatives to comprehensive programs led by dedicated clinicians and PBM committees, employing the use of multiple PBM strategies. Frontline health care professionals play an important role in leading, implementing, operationalizing, measuring, and sustaining successful PBM programs. In this article, we provide practical implementation guidance to support key clinical, administrative, leadership, and structural elements required for the safe and comprehensive delivery of care in PBM programs at the local level.
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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 | 0.146 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".