Renal Dysfunction Is Associated With Poststroke Discharge Disposition and In-Hospital Mortality
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Kidney disease is a frequent comorbidity in patients presenting with acute ischemic stroke. We evaluated whether the estimated glomerular filtration rate (eGFR) on admission is associated with poststroke in-hospital mortality or discharge disposition. METHODS: ). Dialysis was identified by International Classification of Diseases, Ninth Revision codes. Adjusted multivariable Cox proportional hazards models were used to determine the independent associations of eGFR with discharge disposition and in-hospital mortality. Adjusted individual models also examined whether the association of clinical and demographic factors with outcomes varied by eGFR level. RESULTS: Of 232 236 patients, 47.3% had an eGFR ≥60, 26.6% an eGFR 45 to 59, 16.8% an eGFR 30 to 44, 5.6% an eGFR 15 to 29, 0.7% an eGFR<15 without dialysis, and 2.8% were receiving dialysis. Of the total cohort, 11.8% died during the hospitalization or were discharged to hospice, and 38.6% were discharged home. After adjusting for other relevant variables, renal dysfunction was independently associated with an increased risk of in-hospital mortality that was highest among those with eGFR <15 without dialysis (odds ratio, 2.52; 95% confidence interval, 2.07-3.07). An eGFR 15 to 29 (odds ratio, 0.82; 95% confidence interval, 0.78-0.87), eGFR <15 (odds ratio, 0.72; 95% confidence interval, 0.61-0.86), and dialysis (odds ratio, 0.86; 95% confidence interval, 0.79-0.94) remained associated with lower odds of being discharged home. In addition, the associations of several clinical and demographic factors with outcomes varied by eGFR level. CONCLUSIONS: eGFR on admission is an important predictor of poststroke short-term outcomes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".