Bibliographic record
Abstract
In Parkinson’s disease (PD), the development of accurate wearable biomarkers for real-world monitoring is a priority. Developers tend to prioritize agreement with clinical features (e.g., neurological tests). However, wearable biomarkers should also reflect the pathogenic processes underlying these clinical features. This critical aspect is often overlooked in validation studies, raising doubts about construct validity and limiting adoption of these biomarkers. Here, we propose a solution to address this gap. We examined whether a previously validated wearable biomarker, derived from a deep learning model trained on raw accelerometer signals during walking to estimate motor symptom severity scores, can also reflect the pathogenic processes associated with motor dysfunction in people with PD (PwP). The model was reproduced and evaluated in-the-wild, before being deployed on a subset of PwP for whom neuroimaging data were also available. Neuroimaging data were analyzed to extract the brain activity pattern associated with predicted motor symptoms severity scores. The topographic similarity between the extracted pattern and two established brain patterns (one underlying motor symptoms in PD and one not) was assessed. The model accurately estimated ground-truth motor severity scores (mean absolute error = 5.20). Despite not being explicitly trained for this purpose, the model was also able to capture pathogenic mechanisms specifically linked to motor dysfunction in PD (dice similarity = 0.653). These findings represent an initial step toward linking wearable biomarkers not only to clinical features, but also to underlying mechanistic representations. This supports the wider adoption of wearable biomarkers in clinical practice and 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 0.000 |
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".