Recombinant factor VIIa for intractable blood loss after cardiac surgery: a propensity score–matched case‐control analysis
Bibliographic record
Abstract
BACKGROUND: Cardiac surgery is occasionally complicated by massive blood loss that is refractory to standard hemostatic interventions. Recombinant factor VIIa (rF-VIIa) is being increasingly used as rescue therapy in such cases, but little information is available on its safety and efficacy for this indication. STUDY DESIGN AND METHODS: The outcomes of the first 51 cardiac surgery patients who received rF-VIIa for intractable blood loss (from November 2002 to February 2004) at a single institution according to a standardized clinical guideline were compared to 51 matched control patients, with the control patients identified from a large database and matched based on the propensity for massive blood loss. RESULTS: Blood loss and blood product usage were significantly decreased after 2.4 to 4.8 mg of rF-VIIa. In those treated after sternal closure (n = 32), there was a significant reduction in blood loss from the hour before to the hour after treatment: 100 (70, 285) mL (median [25th, 75th percentiles]; p < 0.0001). Except for a slower postoperative recovery and higher incidence of acute renal dysfunction, the adverse event rates were similar between the rF-VIIa-treated patients and their matched controls. CONCLUSIONS: These results suggest that rF-VIIa may be an effective rescue therapy for patients with intractable hemorrhage after cardiac surgery. A clinically important risk of stroke or other major thrombotic complications could not be ruled out by our study. Controlled clinical trials with adequate power to detect the impact of rF-VIIa therapy on morbidity and mortality therefore are necessary before one can recommend its routine use in patients undergoing cardiac surgery who have excessive bleeding.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".