Readmission After Ischemic Stroke in Ningxia, China, From 2017 to 2021: Retrospective Cohort Study
Bibliographic record
Abstract
Background: Stroke remains a major cause of death and disability worldwide. Ischemic stroke is the most common type of stroke. Readmissions after hospitalization increase the patient burden and waste health resources. Objective: This study aimed to calculate rehospitalization rates and explore risk factors associated with rehospitalization in ischemic stroke. Methods: In this retrospective cohort study, we identified 12,782 patients admitted for ischemic stroke at People's Hospital of Ningxia Hui Autonomous Region between January 2017 and December 2021. Groups were determined based on the ID number. The most important factors were selected using the Least Absolute Shrinkage and Selection Operator regression model. Stabilized inverse probability of treatment weighting (SIPTW) was used to correct baseline imbalances between groups. The adjusted hazard ratios and Kaplan-Meier survival curves of significant factors after SIPTW were calculated using stepwise backward Cox regression. Results: A total of 10,727 patients were included in the study. Among them, 12.7% and 7.2% were readmitted within 5 years and 1 year, respectively. Stepwise backward Cox analysis of SIPTW showed that diabetes was the influencing factor for rehospitalization within 5 years (1.15, 1.02-1.30) and 1 year (1.21, 1.03-1.43). Additionally, the female gender was identified as a protective factor against readmission within 5 years (0.83, 0.74-0.93). Conclusions: Although the rate of rehospitalization varied among patients with ischemic stroke at different time points, the significant factors remained consistent. Therefore, early prevention and treatment methods may be consistent.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".