A review of methods used to study cognitive deficits in Parkinson’s disease
Bibliographic record
Abstract
OBJECTIVE: In addition to the classic motor symptoms of Parkinson's disease (PD), some patients suffer from a variety of non-motor symptoms. Cognitive deficits such as impairments to learning and memory have been noted in PD and pose a clinical concern. However, during early stages of the disease these deficits may be subtle and difficult to diagnose. To date, various methodologies have been used to identify and diagnose these impairments in PD; imaging studies, animal models, and computer simulated learning paradigms being the most popular. This review discusses the advantages and disadvantages of each method in studying cognitive deficits associated with PD. RESULTS: Imaging studies, including PET and magnetic resonance imaging scans, are useful when studying neural correlates of cognitive tasks. In contrast, toxin-induced and transgenic animal models are well suited for modelling physiological and behavioural conditions observed in humans. Computer simulated learning paradigms are used to analyze cognitive functioning when one engages in a cognitive task. CONCLUSION: Based on the level of impairment being studied (i.e. neurobiological, behavioural, cognitive basis, or a combination thereof), the use of these methodologies, individually or in conjunction, is imperative when establishing a complete model of PD and its effect on cognition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".