Intraoperative Red Blood Cell Transfusion Decision-making
Bibliographic record
Abstract
OBJECTIVES: The objective of this work was to carry out a systematic review of clinical practice guidelines (CPGs) pertaining to intraoperative red blood cell (RBC) transfusions, in terms of indications, decision-making, and supporting evidence base. SUMMARY OF BACKGROUND DATA: RBC transfusions are common during surgery and there is evidence of wide variability in practice. METHODS: Major electronic databases (MEDLINE, EMBASE, and CINAHL), guideline clearinghouses and Google Scholar were systematically searched from inception to January 2019 for CPGs pertaining to indications for intraoperative RBC transfusion. Eligible guidelines were retrieved and their quality assessed using AGREE II. Relevant recommendations were abstracted and synthesized to allow for a comparison between guidelines. RESULTS: Ten guidelines published between 1992 and 2018 provided indications for intraoperative transfusions. No guideline addressed intraoperative transfusion decision-making as its primary focus. Six guidelines provided criteria for transfusion based on hemoglobin (range 6.0-10.0 g/dL) or hematocrit (<30%) triggers. In the absence of objective transfusion rules, CPGs recommended considering other parameters such as blood loss (n = 7), signs of end organ ischemia (n = 5), and hemodynamics (n = 4). Evidence supporting intraoperative recommendations was extrapolated primarily from the nonoperative setting. There was wide variability in the quality of included guidelines based on AGREE II scores. CONCLUSION: This review has identified several clinical practice guidelines providing recommendations for intraoperative transfusion. The existing guidelines were noted to be highly variable in their recommendations and to lack a sufficient evidence base from the intraoperative setting. This represents a major knowledge gap in the literature.
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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 | 0.034 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".