Bibliographic record
Abstract
PURPOSE OF REVIEW: This review explores recent literature pertaining to affective disorders associated with Parkinson's disease. RECENT FINDINGS: Nonmotor symptoms including affective disorders are becoming more widely recognized as complications of Parkinson's disease. As awareness of these symptoms increases, and new neuroimaging tools are developed and become more accessible, more studies are being conducted pertaining to behavioral complications in Parkinson's disease. The functional connectivity of the basal ganglia can predispose people with Parkinson's to develop affective disorders. Furthermore, dopaminergic treatments may exacerbate or trigger behavioral symptoms. It is now understood that changes associated with Parkinson's disease are widespread, affecting striatal and extrastriatal regions and resulting in alterations in gray matter, white matter, blood flow, metabolism, and dopaminergic and serotonergic function. SUMMARY: Neuroimaging is advancing our knowledge of the mechanisms involved in Parkinson's disease, and their role in the development of behavioral disorders. An increased understanding of these disorders may lead to the discovery of new therapeutic targets, or the identification of risk factors for the development of these disorders. If preventive therapies become available, identification of risk factors will be important for the identification and treatment of susceptible individuals.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".