Bibliographic record
Abstract
Despite all recent advances in symptomatic therapy Parkinson's disease (PD) continues to be a relentlessly progressive neurodegenerative disorder. Therefore therapies that will slow or hold disease progression are a major medical unmet need in PD. Clinical measures of disease progression that have been used in disease modification trials so far have focused on indices of progression of cardinal motor features like bradykinesia, rigidity, and tremor as captured by the UPDRS and the emerging need for effective dopaminergic symptomatic therapy. Progression of global disability in PD, however, is driven by additional factors beyond progressive nigrostriatal denervation leading to increasing severity of cardinal motor features. Progressive pathology in extranigral sites in the brain or peripheral autonomic nervous system contribute to poorly levodopa responsive motor symptoms like postural instability, freezing and falls or nonmotor symptoms. In addition treatment-induced motor complications also impact on PD disability. Although it is widely accepted that clinical progression of PD is multidimensional and in addition includes effects of aging, there is no consensus how to best implement more clinically meaningful endpoints for disease progression trials that would reflect these complex interactions impacting on the evolution of global disability in PD. There is an urgent need for biomarkers for disease progression that would faithfully reflect advancing neurodegeneration and resulted clinical disability in PD and that could be used in shorter term clinical trials testing putative disease modifying agents.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".