Improvements in blood transfusion management: cross-sectional data analysis from nine hospitals in Zhejiang, China
Bibliographic record
Abstract
BACKGROUND: Since 2008, updated perioperative blood management (PoBM) guidelines have been implemented in Zhejiang, China. These guidelines ensure that the limited blood resources meet increasing clinical needs and patient safety requirements. We assessed the effects of implementing updated PoBM guidelines in hospitals in Zhejiang, China. METHODS: We performed a retrospective multicenter study that included adult patients who received blood transfusions during surgical care in the years 2007 and 2011. The volume of allogeneic red blood cells or autologous blood transfusions (cell salvage and acute normovolemic hemodilution [ANH]) for each case was recorded. The rates of performing appropriate pre-transfusion assessments during and after surgery were calculated and compared between the 2 years. RESULTS: We reviewed 270,421 cases from nine hospitals. A total of 15,739 patients received blood transfusions during the perioperative period. The rates of intraoperative allogeneic transfusion (74.8% vs. 49.9%, p < 0.001) and postoperative transfusion (51.9% vs. 44.2%, p < 0.001) both decreased from 2007 to 2011; the rates of appropriate assessment increased significantly during (63.0% vs. 78.0%, p < 0.001) and after surgery (70.6% vs. 78.4%, p < 0.001). The number of patients who received cell salvage or ANH was higher in 2011 (27.6% cell salvage; 9.3% ANH) than in 2007 (6.3% cell salvage; 0.1% ANH). CONCLUSION: Continuing education and implementation of updated PoBM guidelines resulted in significant improvements in the quality of blood transfusion management in hospitals in Zhejiang, China.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".