iPAD-BASED ASSESSMENT IN PARKINSON'S DISEASE
Bibliographic record
Abstract
Background In order to facilitate neuroprotective trials in Parkinson's disease (PD), there is a need for improved means of early disease detection and measuring disease progression. Computerised assessment may afford easier and increasingly accurate administration of motor and cognitive tests, as well as measurement of indices not readily accessible with standard testing paradigms. Aim To develop and validate iPad-based cognitive and motor measures in PD. Method 62 PD patients and 42 age-matched controls completed traditional and iPad versions of the Trail Making Test (TMT) (executive function) and Knox Cube Test (visual memory), as well as a range of other measures. Results Median age was 68 years; median MOCA score was 26. No participant had previously used an iPad. There was significant correlation between traditional and iPad measures: TMTa r=0.74, p<0.001; TMTb r=0.76, p<0.001; Knox r=0.63, p<0.001. Usability data were strong, 90% of participants providing positive feedback. Conclusion This initial study has demonstrated that two iPad-based measures of cognition are acceptable to PD patients and perform similarly to traditional pen-and-paper tests. Further work will extend the analysis of the measured indices in longitudinal studies to determine correlation with disease progression, and extend the battery of iPad-based tests available for PD assessment.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".