Proton magnetic resonance spectroscopy and cognitive impairment in patients with ischemic white matter lesions.
Bibliographic record
Abstract
BACKGROUND: The purpose of this study is to investigate the relationship between the cognitive impairment and NAA/Cr and Cho/Cr ratios in the proton magnetic resonance spectroscopy ((1)HMRS), and to assess the importance of (1)HMRS in the early diagnosis of cognitive impairment in patients with ischemic white matter lesions (WMLs). MATERIALS AND METHODS: A total of 45 patients (23 males and 22 females) with the ischemic WML were divided into mild WML group (n = 15), moderate WML group (n = 15), and severe WML group (n = 15). A total of 15 healthy controls (8 males and 7 females) with no WML on magnetic resonance imaging were included. (1)HMRS focusing on the frontal lobe white matter around the anterior horn of the lateral ventricle and Montreal Cognitive Assessment (MoCA) were conducted. RESULTS: Patients with more severe WML had lower MoCA scores. The NAA/Cr ratio in (1)HMRS was reduced in all the patients and was strongly correlated with the total MoCA scores (r = 0.845, P < 0.001). The Cho/Cr ratio in (1)HMRS was increased in mild and moderate patients, was negatively correlated with the total MoCA scores (r = 0.907, P < 0.001). The Cho/Cr ratio was reduced in the severe patients and was positively correlated with the total MoCA scores (r = 0.937, P < 0.001). In addition, NAA/Cr and Cho/Cr ratios in (1)HMRS were changed in patients with the mild WML whose total MoCA scores were similar to the controls. CONCLUSION: Our results suggest that NAA/Cr and Cho/Cr ratios in (1)HMRS are useful indicators for early diagnosis of ischemic WML and cognitive impairment in patients with ischemic WML.
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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) | 0.001 | 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".