Predictors of 30-day mortality and the risk of recurrent systemic thromboembolism in cancer patients suffering acute ischemic stroke
Bibliographic record
Abstract
BACKGROUND: Stroke in cancer patients is not rare but is a devastating event with high mortality. However, the predictors of mortality in stroke patients with cancer have not been well addressed. D-dimer could be a useful predictor because it can reflect both thromboembolic events and advanced stages of cancer. AIM: In this study, we evaluate the possibility of D-dimer as a predictor of 30-day mortality in stroke patients with active cancer. METHODS: We included 210 ischemic stroke patients with active cancer. The 30-day mortality data were collected by reviewing medical records. We also collected follow-up D-dimer levels in 106 (50%) participants to evaluate the effects of treatment response on D-dimer levels. RESULTS: Of the 210 participants, 30-day mortality occurred in 28 (13%) patients. Higher initial NIHSS scores, D-dimer levels, and CRP levels as well as frequent cryptogenic mechanism, systemic metastasis, multiple vascular territory lesion, hemorrhagic transformation, and larger infarct volume were related to 30-day mortality. In the multivariate analysis, D-dimer [adjusted OR (aOR) = 2.19; 95% CI, 1.46-3.28, P < 0.001] predicted 30-day mortality after adjusting for confounders. The initial NIHSS score (aOR = 1.07; 95% CI, 1.00-1.14, P = 0.043) and hemorrhagic transformation (aOR = 3.02; 95% CI, 1.10-8.29, P = 0.032) were also significant independent of D-dimer levels. In the analysis of D-dimer changes after treatment, the mortality group showed no significant decrease in D-dimer levels, despite treatment, while the survivor group showed the opposite response. CONCLUSIONS: D-dimer levels may predict 30-day mortality in acute ischemic stroke patients with active cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".