A critical appraisal of the premotor symptoms of Parkinson's disease: Potential usefulness in early diagnosis and design of neuroprotective trials
Bibliographic record
Abstract
The neurodegenerative process is well established in Parkinson patients presenting to a physician with early motor signs. There is increasing evidence that a variety of nonmotor features can antedate the typical presentation by many years. As the search for successful disease-modifying treatment advances, it is logical to consider how this could be applied to patients in the earliest stages of the disease, indeed before motor features develop, with the obvious goal of delaying and even preventing the onset of the motor syndrome. However, many of these nonmotor symptoms are rather nonspecific and are not uncommon in the general population. Being able to identify individuals in whom these features are more likely to represent true premotor Parkinson's disease represents a major challenge. Until widely applicable and reliable biomarkers for the presence of Parkinson's disease-related pathology are developed (including biomarkers of disease severity and rate of progression), further evaluation of possible premotor features in selected populations will probably serve as the basis for future studies of disease-modifying therapies. This article will review the current status of premotor symptoms of Parkinson's disease and discuss their potential for early diagnosis and the design of neuroprotective trials.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".