Fibrin sealant use for minimising peri-operative allogeneic blood transfusion
Bibliographic record
Abstract
BACKGROUND: Fibrin sealants have gained increasing popularity as interventions to improve peri-operative (intra/post-operative) haemostasis and diminish the need for allogeneic red cell transfusion (blood from an unrelated donor). OBJECTIVES: To examine the efficacy of fibrin sealants in reducing peri-operative blood loss and allogeneic red blood cell (RBC) transfusion. SEARCH STRATEGY: Studies were identified by: searches of MEDLINE, EMBASE, Current Contents, the Cochrane Library (July 2002), manufacturer web sites (to July 2002), and bibliographies of published articles. SELECTION CRITERIA: Controlled trials in which adult patients, scheduled for elective surgery, were randomised to fibrin sealant treatment or to a control group who did not receive fibrin sealant treatment. Trials were eligible if they reported data on the number of patients exposed to allogeneic red cell transfusion, the volume of blood transfused, or blood loss (assessed objectively). DATA COLLECTION AND ANALYSIS: Primary outcomes measured were: the number of patients exposed to allogeneic red cells, the amount of blood transfused, and blood loss. Other outcomes measured were: re-operation due to bleeding, infection, mortality, and length of hospital stay. Treatment effects were pooled using a random effects model. MAIN RESULTS: Seven trials, including a total of 388 patients, reported data on peri-operative exposure to allogeneic RBC transfusion. Fibrin sealant treatment, on average, reduced the rate of exposure to allogeneic red cell transfusion by a relative 54% (relative risk [RR] = 0.46: 95%CI = 0.32 to 0.68). Eight trials, including a total of 442 patients, provided data for post-operative blood loss. Fibrin sealant treatment reduced blood loss on average by around 134 per patient (95%CI = 51 to 217 ). However the trials reviewed were small and of poor methodological quality (91% unblinded). REVIEWER'S CONCLUSIONS: Overall the results suggest that fibrin sealants are efficacious in reducing both post-operative blood loss and peri-operative exposure to allogeneic RBC transfusion. However, due to the lack of blinding, transfusion practices may have been influenced by knowledge of the patient's treatment status. This raises concerns about the use of blood transfusion practice as an outcome variable in trials of fibrin sealant. In the case of blood loss, the results must be interpreted with caution, in view of the statistically significant heterogeneity in treatment effect observed. Large, methodologically rigorous, randomised controlled trials of fibrin sealants are needed.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| 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".