Retained Blood Syndrome after Cardiac Surgery: A New Look at an Old Problem
Bibliographic record
Abstract
Retained blood occurs when drainage systems fail to adequately evacuate blood during recovery from cardiothoracic surgery. As a result, a spectrum of mechanical and inflammatory complications can ensue in the acute, subacute, and chronic setting. The objectives of this review were to define the clinical syndrome associated with retained blood over the spectrum of recovery and to review existing literature regarding how this may lead to complications and contributes to poor outcomes. To better understand and prevent this constellation of clinical complications, a literature review was conducted, which led us to create a new label that better defines the clinical entity we have titled retained blood syndrome. Analysis of published reports revealed that 13.8% to 22.7% of cardiac surgical patients develop one or more components of retained blood syndrome. This can present in the acute, subacute, or chronic setting, with different pathophysiologic mechanisms active at different times. The development of retained blood syndrome has been linked to other clinical outcomes, including the development of postoperative atrial fibrillation and infection and the need for hospital readmission. Grouping multiple objectively measurable and potentially preventable postoperative complications that share a common etiology of retained blood over the continuum of recovery demonstrates a high prevalence of retained blood syndrome. This suggests the need to develop, implement, and test clinical strategies to enhance surgical drainage and reduce postoperative complications in patients undergoing cardiothoracic surgery.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".