Multimodal Patient Blood Management Program Based on a Three-pillar Strategy
Bibliographic record
Abstract
OBJECTIVES: To determine whether a multidisciplinary, multimodal Patient Blood Management (PBM) program for patients undergoing surgery is effective in reducing perioperative complication rate, and thereby is effective in improving clinical outcome. BACKGROUND: PBM is a medical concept with the focus on a comprehensive anemia management, to minimize iatrogenic (unnecessary) blood loss, and to harness and optimize patient-specific physiological tolerance of anemia. METHODS: A systematic review and meta-analysis was performed. Eligible studies had to address each of the 3 PBM pillars with at least 1 measure per pillar, for example, preoperative anemia management plus cell salvage plus rational transfusion strategy. The study protocol has been registered with PROSPERO (CRD42017079217). RESULTS: Seventeen studies comprising 235,779 surgical patients were included in this meta-analysis (100,886 pre-PBM group and 134,893 PBM group). Implementation of PBM significantly reduced transfusion rates by 39% [risk ratio (RR) 0.61, 95% confidence interval (CI) 0.55-0.68, P < 0.00001], 0.43 red blood cell units per patient (mean difference -0.43, 95% CI -0.54 to -0.31, P < 0.00001), hospital length of stay (mean difference -0.45, 95% CI -0.65 to -0.25, P < 0,00001), total number of complications (RR 0.80, 95% CI 0.74-0.88, P <0.00001), and mortality rate (RR 0.89, 95% CI 0.80-0.98, P = 0.02). CONCLUSIONS: Overall, a comprehensive PBM program addressing all 3 PBM pillars is associated with reduced transfusion need of red blood cell units, lower complication and mortality rate, and thereby improving clinical outcome. Thus, this first meta-analysis investigating a multimodal approach should motivate all executives and health care providers to support further PBM activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".