Implementation of patient blood management remains extremely variable in Europe and Canada
Bibliographic record
Abstract
BACKGROUND: Preoperative anaemia is associated with increased postoperative morbidity and mortality. Patient blood management (PBM) is advocated to improve patient outcomes. OBJECTIVES: NATA, the 'Network for the advancement of patient blood management, haemostasis and thrombosis', initiated a benchmark project with the aim of providing the basis for educational strategies to implement optimal PBM in participating centres. DESIGN: Prospective, observational study with online data collection in 11 secondary and tertiary care institutions interested in developing PBM. SETTING: Ten European centres (Austria, Spain, England, Denmark, Belgium, Netherlands, Romania, Greece, France, and Germany) and one Canadian centre participated between January 2010 and June 2011. PATIENTS: A total of 2470 patients undergoing total hip (THR) or knee replacement, or coronary artery bypass grafting (CABG), were registered in the study. Data from 2431 records were included in the final analysis. MAIN OUTCOME MEASURES: Primary outcome measures were the incidence and volume of red blood cells (RBC) transfused. Logistic regression analysis identified variables independently associated with RBC transfusions. RESULTS: The incidence of transfusion was significantly different between centres for THR (range 7 to 95%), total knee replacement (range 3 to 100%) and CABG (range 20 to 95%). The volume of RBC transfused was significantly different between centres for THR and CABG. The incidence of preoperative anaemia ranged between 3 and 40% and its treatment between 0 and 40%, the latter not being related to the former. Patient characteristics, evolution of haemoglobin concentrations and blood losses were also different between centres. Variables independently associated with RBC transfusion were preoperative haemoglobin concentration, lost volume of RBC and female sex. CONCLUSION: Implementation of PBM remains extremely variable across centres. The relative importance of factors explaining RBC transfusion differs across institutions, some being patient related whereas others are related to the healthcare process. The results reported confidentially to each centre will allow them to implement tailored measures to improve their PBM strategies.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".