The Changes of Cerebral White Matter MR Diffusion Tensor Imaging of Brain on Patients of Mild Cognitive Impairment and Its Relationship with Cognitive Dysfunction
Bibliographic record
Abstract
Objective:To analyze the changes of cerebral white matter MR diffusion tensor imaging of brain on patients of mild cognitive impairment and the correlation with cognitive dysfunction.Method:120 patients with mild cognitive impairment admitted in our hospital from July 2012 to July 2014 were selected as the study object, in accordance with no deep white matter ischemia were divided into ischemia group(n=78) and non myocardial ischemia group(n=42), and 30 cases of healthy normal people were selected as control group, the three groups object were respectively given conventional magnetic resonance diffusion tensor imaging and detection, and using Mo CA(Montreal cognitive) scale and MMSE(mental state) rating scale evaluated mental status of the object and cognitive function, and cognition of white matter changes and function correlation between barrier in brain of three groups were observed.Result:The function of ischemic group cognitive outcome score of(20.29±2.00), was significant lower than the ischemia group(22.11±2.26), the comparison between the two groups had obvious difference(P0.05); compared with non ischemia group, ischemia group frontal ADC were increased, FA value decreased significantly(t=4.228, 3.250, P0.05), parietal ADC value increased, FA value decreased obviously(t=2.701, 7.532, P0.05); compared with the control group, non ischemia group frontal ADC increased, FA decreased(t=7.791, 2.874, P0.05), parahippocampal gyrus ADC upgraded, FA decreased(t=4.414, 5.163, P 0.05).Conclusion:In clinical diagnosis of patients with MCI, using MR diffusion tensor imaging detection can quantifiable analysis of cerebral white matter lesions level of patients, determine the possibility of conversion to Alzheimer's disease, and provide guidance for early treatment of patients, is worthy of promotion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".