A correlation study of Magnetic Resonance Spectroscopy (MRS) metabolite ratios with cognitive function in ischemic stroke patients
Bibliographic record
Abstract
Background: Post-stroke cognitive impairment (PSCI) is one of the complications of ischemic stroke that can highly impact the patient’s quality of life and requires prompt detection and management for optimal outcomes. Magnetic resonance spectroscopy (MRS) may be a potential diagnostic tool for PSCI due to its capacity to evaluate metabolite parameters. The aim of this study was to assess the correlation of various MRS metabolite ratios with cognitive function in ischemic stroke patients. Methods: A cross-sectional study was performed on ischemic stroke patients with an onset of 24 hours – 7 days at Dr. Wahidin Sudirohusodo Central General Hospital, Makassar. MRS was used to determine metabolite ratios (NAA, Cho, Lac, and mIns to Cr) by voxel placement in ischemic lesions. Cognitive function was evaluated using the Montreal Cognitive Assessment Indonesian version (MoCA-Ina) instrument. Spearman’s test was used to determine the correlation. Results: The study included 23 patients, with a mean age of 54.61 years (SD = 11.96) and 65.22% male. The Cho/Cr ratio had a significant, moderate correlation with cognitive function (r = 0.47; p = 0.02). Other metabolites, such as NAA/Cr, Lac/Cr, and mIns/Cr, did not correlate with cognitive function. Conclusion: In ischemic stroke patients, the Cho/Cr ratio was significantly correlated with 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 | 0.001 | 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 | 0.000 |
| 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".