Risk factors for red cell transfusion in adults undergoing coronary artery bypass surgery: a systematic review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Identifying factors that can predict adults at high risk of receiving red blood cell transfusion during coronary artery bypass graft (CABG) surgery may aid in more efficient blood banking practices and may tailor blood conservation strategies for these adult patients. The objective was to identify clinical factors associated with increased red cell transfusion in adults undergoing CABG surgery. METHODS: A systematic review of the MEDLINE and HealthSTAR databases from 1966 to December 2005 was conducted. Citations containing the medical subject heading or textwords 'coronary artery bypass graft', 'CABG' and 'cardiovascular surgery' were combined with the medical subject headings or textwords 'transfusion' and 'blood transfusion'. RESULTS: A total of 2461 abstracts were retrieved. Twenty-one studies met the inclusion/exclusion criteria. Transfusion rates ranged from 7 to 97%. Several variables were identified that were associated with increased red cell transfusion rates including older age, female sex, low haemoglobin concentration or haematocrit value, renal insufficiency and urgent/emergent surgery. The strongest risk factor was the urgency of surgery (urgent or emergent surgery), which was associated with a 4x to 8x increase in transfusion rates compared to elective surgery. Increasing age and female sex increased the likelihood of transfusion by 1x to 3x and 2x, respectively. CONCLUSIONS: Increasing patient age, female sex, lower preoperative haemoglobin levels, as well as the urgency of the CABG surgery were associated with higher transfusion rates. Identifying risk factors for transfusion may allow for targeted use of blood conservation strategies, improved efficiency in blood utilization and informing adults at risk of transfusion.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".