Bibliographic record
Abstract
Parkinson's disease is highly heterogeneous in early clinical features and later outcomes. This makes classifying subgroups of PD relevant to clinical research and practice, particularly if they are prognostically relevant. Subgroups have been defined both on the basis of motor and nonmotor features, and subgroups have been determined either empirically, based on clinical observation, or using data-driven analytic techniques. Previous studies have examined both the overall number and the nature of nonmotor symptoms and signs in tremor-dominant compared with non-tremor-dominant subtypes, and longitudinal studies identify nonmotor symptoms as important markers of prognosis and important defining features of PD subtypes. Autonomic features seem to preferentially affect individuals with non-tremor-dominant PD subtype early in the disease. Later in the disease cognitive disturbance distinguishes this phenotype. Pathological and neuroimaging studies provide substantial evidence for fundamental biological differences between tremor-dominant and postural instability gait disorder/akinetic-rigid subtypes. Biomarker studies point toward non-tremor-dominant PD as representing more advanced and diffuse neurodegeneration than tremor-dominant PD, encompassing dopaminergic and nondopaminergic as well as synuclein and nonsynuclein (Abeta) pathologies. This aligns with clinical studies that find a higher burden of nonmotor symptoms in non-tremor-dominant PD. The mounting evidence for the relevance of nonmotor features in PD subtypes behooves us to begin to investigate the biological underpinnings of subtypes defined by both motor and nonmotor features. This may be challenging, as PD subtypes are unlikely to be distinct nonoverlapping entities but are more likely to represent typical phenotypes within a multidimensional spectrum resulting from variable contributions of a number of simultaneous pathological processes. © 2016 International Parkinson and Movement Disorder Society.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".