Frontal vs. Posterior cognitive dysfunction: Does greater risk of dementia lead to a differential gait in Parkinson’s disease?
Bibliographic record
Abstract
Background: Gait impairment is suggested to predict the onset of dementia in Parkinson’s disease (PD). Interestingly, studies have shown that PD patients with cognitive deficits mediated by posterior brain areas are at greater risk of developing dementia than those with frontal deficits. Yet, it remains unknown whether PD patients with posterior cognitive deficits show differences in gait when compared to those with frontal deficits. Aim: This study aimed to compare gait of individuals with PD showing “posterior”, “frontal”, or no cognitive impairment (NCI). Methods: Based on a sample of 64 individuals with PD, median scores were calculated for three neuropsychological tests relying on “frontal” and three relying on “posterior” brain areas. Individuals assigned into the Frontal or Posterior groups showed at least 2 out of 3 scores lower than the median in frontal or posterior tests, respectively. Those with 0 or 1 score lower than the median were classified as NCI. Participants walked under single and dual task conditions. Results: All groups walked slower, with greater variability, wider base of support, and longer double support in the dual task condition. Interpretation: PD patients with posterior cognitive deficits walk similarly to those with frontal deficits and those with normal cognition.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".