Clinical and Diffusion Tensor Imaging to Evaluate Falls, Balance and Gait Dysfunction in Leukoaraiosis: an Observational, Prospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To assess the correlation between leukoaraiosis (LA) and falls, to determine the risk factors for falls in patients with LA, and to detect specific white matter tracts are associated with the falls by using the diffusion tensor magnetic resonance imaging (DTI) screen. METHODS: For the elderly patients with LA, we collected demographic information and scores for the Tinetti Balance and Gait Scale, Berg Balance Scale, Timed up-and-go test, and Cognitive, Emotional, Sleep-related Scale. All the patients underwent DTI scanning and were followed up for 1 year. RESULTS: Ninety-four individuals were prospectively enrolled. After multivariable analyses, age, history of falls in the past year, antidepressants usage, and LA-Fazekas grade were reported to be risk factors for falls. In patients with severe LA, the fall incidence was higher than in those with mild LA. Tract-Based Spatial Statistics showed that fractional anisotropy values of the corpus callosum, cingulate gyrus, anterior limb of internal capsule, cerebral peduncle, anterior corona, and fronto-occipital fasciculus were significantly reduced in the patients who fell. The body of the corpus callosum and anterior corona radiate were significantly related to balance and gait function. CONCLUSIONS: Our findings indicated that age, history of falls in the past year, antidepressants usage, and LA-Fazekas grade were risk factors for falls in elderly patients with LA. Leukoaraiosis was relevant for falls, but LA severity had a threshold effect with falls. The loss of integrity of some white matter tracts might influence balance and gait function. The DTI had preeminent clinical application prospects for identifying fall risk in patients with LA.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".