Difference of cognitive impairment in ischemic stroke patients based on hemiparesis side at Atma Jaya Hospital, Indonesia, 2014‐2018
Bibliographic record
Abstract
Abstract Background Cognitive impairment in stroke patients can decrease their quality of life. The characteristics of cognitive impairment may be different based on the lesion’s location. Objective was to assess the difference of cognitive function in ischemic stroke patients based on hemiparesis side. Method A cross‐sectional research on 125 ischemic stroke patients in Atma Jaya Hospital 2014‐2018. Analyzed data includes age, hemiparesis side, MMSE and MoCA‐INA scores. Data was analyzed using univariate and bivariate Mann‐Whitney test. Result Patients with left hemiparesis had higher scores in all categories of MMSE and MoCA‐INA than patients with right hemiparesis. In patients aged less than 55 years old, MoCA‐INA results showed that left hemiparesis had a significantly higher language score (p=0,030); but naming, delayed recall, and orientation scores were higher in right hemiparesis. In patients aged more than 55 years old, scores for visuospatial in MMSE and language in MoCA‐INA were higher in right hemiparesis. Conclusion Overall scores for MMSE and MoCA‐INA in left hemiparesis were higher than right hemiparesis, although not significant. Language ability measured with MoCA‐INA was significantly higher in left hemiparesis in patients aged less than 55 years old.
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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 | 0.000 |
| 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".