Association of blood pressure and cognitive function based on MoCA‐INA and MMSE scores among ischemic stroke patients at Atma Jaya Hospital in 2014‐2019
Bibliographic record
Abstract
Abstract Background Cognitive impairment is a common finding among stroke patients and causing significant decline in survivors’ quality of life. Hypoperfusion is one of the mechanism of transient or permanent ischemia and cause of cognitive deficit. Hypertension causes right shift on cerebrovascular autoregulation, causing brain to be more vulnerable to hypoperfusion in hypotensive state. However, studies assessing the association of blood pressure on acute phase of stroke and cognitive function are still scarce. Method A cross‐sectional study using secondary data from Atma Jaya Hospital Stroke Registry in 2014‐2019. Total sampling method resulted in 144 eligible samples. Blood pressure measurement was done at the time of admission, cognitive function was assessed at the time of discharge using MoCA‐INA and MMSE questionnaires. Data analysis was performed using Kruskall‐Wallis comparative test. Result No significant association of systolic blood pressure and cognitive function based on MoCA‐INA (p = 0,569) and MMSE (p = 0,147) scores. No significant association of diastolic blood pressure and cognitive function based on MoCA‐INA (p = 0,818) and MMSE (p = 0,184) scores. Conclusion No significant association of blood pressure on acute phase of stroke and cognitive function based on MoCA‐INA and MMSE scores among ischemic stroke patients at Atma Jaya Hopital in 2014‐2019.
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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 | 0.001 |
| 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 | 0.000 | 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".