Initial exploration on clinical application of resting-state functional magnetic resonance imaging amplitude of low-frequency fluctuation method on cognitive impairment in amyotrophic lateral sclerosis
Bibliographic record
Abstract
Objective To explore diagnostic value of amplitude of low-frequency fluctuation (ALFF) on cognitive impairment in amyotrophic lateral sclerosis (ALS) using resting-state functional magnetic resonance imaging (MRI). Methods Sixteen ALS patients from neurological clinic in Peking Union Medical College Hospital were enrolled between November 2013 and April 2015. The patients were divided into two groups by the presence (ALSi, n=7) or absence (ALSu, n=9) of cognitive impairment. Routine MRI structural images and resting-state functional MRI were collected for comparison between groups through voxel-based morphometry (VBM) and ALFF. Results (1) Neuropsychological analysis showed significant differences in Montreal Cognitive Assessment score (22.9±2.0 vs 25.8±2.3, t=2.622, P=0.020), Frontal Assessment Battery score (12.4±1.6 vs 15.1±1.4, t=3.600, P=0.003), animal listing test (13.6±1.8 vs 16.7±2.9, t=2.482, P=0.026), naming test (2(1) vs 0(1), Z=-2.746, P=0.006), similarity test (7.9±3.7 vs 17.3±2.8, t=5.846, P=0.000)and clock drawing test (2(2) vs 3(0), Z=2.516, P=0.012). (2) VBM analysis showed no significant differences in both gray matter and white matter density between the two groups. (3) ALFF analysis showed significantly increased signals in widespread areas of bilateral cerebrum and cerebellum in ALSi group compared to ALSu group. Conclusion ALFF value has the potential to provide more valuable imaging basis for early diagnosis on cognitive impairment in ALS. Key words: Amyotrophic lateral sclerosis; Cognition disorders; Magnetic resonance imaging; Diagnosis
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".