Patient-centered development of clinical outcome assessments in early Parkinson disease: key priorities and advances
Bibliographic record
Abstract
Parkinson disease (PD) is a progressive neurodegenerative disorder with a societal burden expected to increase rapidly. In some countries, PD incidence could double in the next 50 years 1 . It is well-established that in the years preceding the emergence of parkinsonian features that currently enable a clinical diagnosis of PD 2 , various motor and non-motor manifestations such as REM sleep behavior disorder (RBD), hyposmia or constipation 3 become apparent as an expression of the underlying biology. Although dopaminergic therapy changed dramatically the natural history of the disease and improved the quality of life of those living with clinical PD, these symptomatic treatments are often associated with side effects such as motor complications, and their effectiveness can be reduced over time (or become ineffective) for some features of PD, such as gait and balance. In addition, there is a lack of effective therapies for many non-motor features of PD. There is no therapy able to slow down, halt, or reverse PD progression. In the last 12 months, the field has converged on the urgent need to develop a biological definition and staging/classification of PD to enable therapeutic intervention studies that target the relevant biological process(es), ideally before the earliest clinical signs of parkinsonism 4 , 5 . Biologically targeted therapies are expected to delay the emergence of key disease milestones such as the presence of diagnostic motor features, treatment-associated motor complications, postural instability, dysautonomia, or dementia 6 . A direct implication of being able to diagnose the underlying biology before the emergence of a diagnostic parkinsonism is the need to measure the earliest clinical changes to evaluate the impact of therapeutic interventions earlier in the disease using outcomes that are relevant to those with a lived experience. In summary, regulatory-accepted clinical outcome assessments (COAs) sensitive to clinical changes in early biological disease are urgently needed.
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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 | 0.109 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".