The independent association of massive blood loss with mortality in cardiac surgery
Bibliographic record
Abstract
BACKGROUND: Although the association between massive perioperative blood loss (MBL) and adverse outcomes is well recognized, it is unclear whether MBL is an independent risk factor or, instead, simply a marker for other adverse events or severity of illness. The objective of this cohort study was to quantify the independent association of MBL in cardiac surgery with all-cause in-hospital mortality. STUDY DESIGN AND METHODS: Data were prospectively collected on consecutive patients who underwent cardiac surgery with cardiopulmonary bypass at a quaternary-care academic center from 1999 to 2003. The number of red blood cell (RBC) units transfused within 1 day of surgery was used as a surrogate measure of perioperative blood loss. Receiver-operating characteristic curve analyses were employed to identify the most appropriate cutoff for defining MBL. The independent association of MBL with mortality was determined with multivariable logistic regression analyses. Bootstrapping and sensitivity analyses were used to confirm the validity of the results. RESULTS: MBL was defined as receiving at least 5 units of RBCs within 1 day of surgery. Of 9215 patients analyzed, 1.8 percent (n = 169) died and 9.7 percent (n = 890) had MBL. After adjusting for multiple potential confounders (including perioperative adverse events), MBL was associated with an 8.1-fold (95% confidence interval, 3.9-17.0) increase in the odds of death. This risk estimate was stable across different modeling conditions as well as in bootstrap sampling. CONCLUSION: MBL after cardiac surgery has a strong, independent association with in-hospital mortality.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".