One-year survival and prognostic factors for survival among stroke patients
Bibliographic record
Abstract
Background: Survival and prognostic factors following stroke occurrence differ between world regions. Studies investigating stroke features in the Middle-east region are scarce. We aimed to investigate 1-year survival and related prognostic factors of stroke patients in Central Iran. Materials and Methods: It is an observational analytical study conducted on patients registered in the Persian Registry of Cardiovascular Disease-Stroke (PROVE-Stroke) database. Records of 1703 patients admitted during 2015-2016 with the primary diagnosis of stroke in all hospitals of Isfahan, Iran were reviewed. Information regarding sociodemographic characteristics, clinical presentations, medications, and comorbidities were recorded. The living status of patients after 1 year from stroke was considered as 1-year survival. Results: Among 1345 patients with the final diagnosis of stroke, 970 (72.1%) were alive at the 1 year follow-up and the mean survival time based on Kaplan-Meier procedure was estimated 277.33 days. The hemorrhagic and ischemic types of stroke were reported in 201 (15.0%) and 1141 (84.8%) patients, respectively. Age (hazard ratio [HR] = 1.07, 95% confidence interval [CI] = 1.05-1.09), diabetes (HR = 1.49, 95% CI = 1.07-2.06), history of stroke or transient ischemic attack (HR = 1.81, 95% CI = 1.30-2.52), history of warfarin usage (HR = 1.73, 95% CI = 1.11-2.71), hospital complications of hemorrhage (HR = 3.89, 95% CI = 2.07-7.31), sepsis (HR = 1.78, 95% CI = 1.18-2.68), and hydrocephalus (HR = 3.43, 95% CI = 1.34-8.79), and modified Rankin Scale (mRS) ≥3 at the time of hospital dicharge (HR = 1.98, 95% CI = 1.27-3.07), were predictors of 1-year survival. Conclusion: Predictors of 1-year survival can be categorized into unchangeable ones, such as age, diabetes, previous stroke, and mRS. The changeable factors, such as hospital complications of infection and hemorrhage, guide physicians to pay greater attention to reduce the risk of mortality following stroke.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".