Correlation of electrolytes with falling risk, cognitive function, and functional outcome in acute ischemic stroke patient
Bibliographic record
Abstract
Stroke outcome is determined on multiple factors. However, there are limited studies discussing the impact of electrolyte imbalance on stroke outcome. In this study, we analyzed sodium, calcium, and potassium level in acute ischemic stroke, and compare their risk of falling, cognitive function, and functional outcome. This was a cross-sectional study in Dr. Moewardi General Hospital, Indonesia between January and June 2023. Patient with acute ischemic stroke were enrolled in this study. Cognitive function was assessed with mini mental state examination (MMSE) and the Indonesian version of montreal cognitive assessment (MoCA-Ina). National Institutes of Health Stroke Scale (NIHSS), Modified Rankin Scale (MRS) and Morse Fall Score (MFS) were used to assessed stroke severity, disability, and risk of falling, respectively. Pearson correlation was then performed to evaluate the correlation of electrolytes level with MMSE, MoCA-Ina, NIHSS, MRS, and MFS. Furthermore, we also analyzed the odds ratio of increasing risk of falling, cognitive function deterioration, and worse functional outcome. A p-value of <0.05 is considered statistically significant. On univariate analysis, natrium is correlated with MMSE (r=0.174; p=0.042), NIHSS (r=-0.412; p=0.011), MRS (r=-0.174; p=0.042), and MFS (r=-0.304; p=0.042). Potassium is correlated with MMSE (r=0.344; p=0.044), MoCA-INA (r=0.341; p=0.048), NIHSS (r=-0.572; p=0.019), (MRS r=-0.376; p=0.017), and MFS (r=-0.612; p=0.031). Calcium is correlated with NIHSS r=-0.348 (p=0.018), MRS r=-0.256 (p=0.036). On odds ratio analysis, low natrium level increased the risk of deteriorating cognitive function, and low level of potassium increased the risk of falling. Electrolyte imbalances correlates with risk of falling and deteriorating cognitive function.
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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 | 0.000 | 0.002 |
| 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".