Predicting mortality in patients with disseminated intravascular coagulation after cardiopulmonary bypass surgery by utilizing two scoring systems
Bibliographic record
Abstract
: We evaluated clinical and laboratory biomarkers of disseminated intravascular coagulation (DIC) following cardiac surgery in the cardiothoracic surgical ICU (CTICU) to predict mortality. We retrospectively analyzed CTICU patients with suspected DIC identified from the hospital laboratory database, and calculated International Society on Thrombosis and Haemostasis (ISTH) and the Japanese Association for Acute Medicine (JAAM) DIC scores to predict DIC-related mortality. The predictive accuracy of the JAAM and ISTH DIC scoring system were then assessed by logistic regression analysis and receiver operative characteristics analysis, and compared to other potential predictors of mortality (e.g., Acute Physiology and Chronic Health Evaluation II, systemic inflammatory response syndrome criteria, laboratory variables). Our study showed a 30-day mortality rate of 71% in CTICU patients with DIC. The JAAM DIC score offered the best predictive accuracy [area under the curve (AUC): 0.723, 95% % confidence interval (CI): 0.638-0.947, P = 0.021], when compared with ISTH DIC score (AUC: 0.707, 95% CI: 0.491-0.923, P = 0.066) and Acute Physiology and Chronic Health Evaluation II (AUC: 0.687, 95% CI: 0.483-0.891, P = 0.110). A JAAM DIC score at least 6 was reported in 89% of the nonsurvivors and 46% of survivors (P = 0.010), and predicted mortality [odds ratio: 9.33 (1.50-58.20)] with a 73% sensitivity and a 78% specificity. Our results also show a strong relationship between acid-base derangement and mortality. This initial evaluation of DIC-related mortality in the CTICU found the standardized JAAM DIC scoring system in combination with acid-base laboratory values were most useful to predict mortality in postcardiac surgery patients with DIC. Additional prospective studies are needed to further validate our findings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".