Should Cell Salvage Be Used in Liver Resection and Transplantation? A Systematic Review and Meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of intraoperative blood cell salvage and autotransfusion (IBSA) use on red blood cell (RBC) transfusion and postoperative outcomes in liver surgery. BACKGROUND: Intraoperative RBC transfusions are common in liver surgery and associated with increased morbidity. IBSA can be utilized to minimize allogeneic transfusion. A theoretical risk of cancer dissemination has limited IBSA adoption in oncologic surgery. METHODS: Electronic databases were searched from inception until May 2021. All studies comparing IBSA use with control in liver surgery were included. Screening, data extraction, and risk of bias assessment were conducted independently, in duplicate. The primary outcome was intraoperative allogeneic RBC transfusion (proportion of patients and volume of blood transfused). Core secondary outcomes included: overall survival and disease-free survival, transfusion-related complications, length of hospital stay, and hospitalization costs. Data from transplant and resection studies were analyzed separately. Random effects models were used for meta-analysis. RESULTS: Twenty-one observational studies were included (16 transplant, 5 resection, n=3433 patients). Seventeen studies incorporated oncologic indications. In transplant, IBSA was associated with decreased allogeneic RBC transfusion [mean difference -1.81, 95% confidence interval (-3.22, -0.40), P =0.01, I 2 =86%, very-low certainty]. Few resection studies reported on transfusion for meta-analysis. No significant difference existed in overall survival or disease-free survival in liver transplant [hazard ratio (HR)=1.12 (0.75, 1.68), P =0.59, I 2 =0%; HR=0.93 (0.57, 1.48), P =0.75, I 2 =0%] and liver resection [HR=0.69 (0.45, 1.05), P =0.08, I 2 =0%; HR=0.93 (0.59, 1.45), P =0.74, I 2 =0%]. CONCLUSION: IBSA may reduce intraoperative allogeneic RBC transfusion without compromising oncologic outcomes. The current evidence base is limited in size and quality, and high-quality randomized controlled trials are needed.
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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.036 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".