Plasma D-dimer may predict poor functional outcomes through systemic complications after aneurysmal subarachnoid hemorrhage
Bibliographic record
Abstract
OBJECTIVE Plasma D-dimer levels elevate during acute stages of aneurysmal subarachnoid hemorrhage (SAH) and are associated with poor functional outcomes. However, the mechanism in which D-dimer elevation on admission affects functional outcomes remains unknown. The aim of this study is to clarify whether D-dimer levels on admission are correlated with systemic complications after aneurysmal SAH, and to investigate their additive predictive value on conventional risk factors for poor functional outcomes. METHODS A total of 187 patients with aneurysmal SAH were retrospectively analyzed from a single-center, observational cohort database. Correlations of plasma D-dimer levels on admission with patient characteristics, initial presentation, neurological complications, and systemic complications were identified. The authors also evaluated the additive value of D-dimer elevation on admission for poor functional outcomes by comparing predictive models with and without D-dimer. RESULTS D-dimer elevation on admission was associated with increasing age, female sex, and severity of SAH. Patients with higher D-dimer levels had increased likelihood of nosocomial infections (OR 1.22 [95% CI 1.07-1.39], p = 0.004), serum sodium disorders (OR 1.11 [95% CI 1.01-1.23], p = 0.033), and cardiopulmonary complications (OR 1.20 [95% CI 1.04-1.37], p = 0.01) on multivariable analysis. D-dimer elevation was an independent risk factor of poor functional outcome (modified Rankin Scale Score 3-6, OR 1.50 [95% CI 1.15-1.95], p = 0.003). A novel prediction model with D-dimer had significantly better discrimination ability for poor outcomes than conventional models without D-dimer. CONCLUSIONS Elevated D-dimer levels on admission were independently correlated with systemic complication, and had an additive value for outcome prediction on conventional risk factors after aneurysmal SAH.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".