Structural Neuroimaging Markers of Cognitive Decline in Parkinson’s Disease
Bibliographic record
Abstract
Cognitive impairment in patients with Parkinson's disease is a major challenge since it has been established that 25 to 40% of patients will develop cognitive impairment early in the disease. Furthermore, it has been reported that up to 80% of Parkinsonian patients will eventually develop dementia. Thus, it is important to improve the diagnosing procedures in order to detect cognitive impairment at early stages of development and to delay as much as possible the developing of dementia. One major challenge is that patients with mild cognitive impairment exhibit measurable cognitive deficits according to recently established criteria, yet those deficits are not severe enough to interfere with daily living, hence being avoided by patients, and might be overseen by clinicians. Recent advances in neuroimaging brain analysis allowed the establishment of several anatomical markers that have the potential to be considered for early detection of cognitive impairment in Parkinsonian patients. This review aims to outline the neuroimaging possibilities in diagnosing cognitive impairment in patients with Parkinson's disease and to take into consideration the near-future possibilities of their implementation into clinical practice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".