Identifying prodromal Parkinson's disease: Pre‐Motor disorders in Parkinson's disease
Bibliographic record
Abstract
Increasing recognition that Parkinson's disease (PD) may start outside of the substantia nigra has led to a rapidly expanding effort to define prodromal stages of PD, before motor signs permit classical diagnosis. Many of these efforts center around the identification of clinical non-motor symptoms and signs of disease. There is now direct evidence that olfaction, rapid eye movement (REM) sleep behavior disorder (RBD), constipation, and depression can be present in prodromal PD. In addition, there is suggestive evidence that visual changes, other autonomic symptoms, and subtle cognitive changes may also be present at prodromal stages. A critical issue in utility of these prodromal markers will be assessment of sensitivity, specificity, and positive and negative predictive values. Although these have yet to be fully defined, olfactory deficits, some visual changes, and autonomic symptoms occur in the majority of PD patients at diagnosis, suggesting good potential sensitivity. However, with the exception of RBD and perhaps some specific autonomic measures, specificity, and positive predictive value of these markers may be insufficient to be used alone as identifiers of prodromal disease. The evidence for the utility of olfaction, RBD, autonomic markers, visual changes, mood disorders, and cognitive loss as markers of prodromal PD and the potential sensitivity and specificity of these markers are summarized.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| 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) | 0.003 | 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".