Correlation between Tapping and Inserting of Pegs in Parkinson's Disease
Bibliographic record
Abstract
BACKGROUND: Various investigators have developed complex quantitative instrumental procedures for objective assessment of parkinsonian motor impairment, since drawbacks of rating scales are interrater variability, subjective impression, and insensitivity to subtle modifications. OBJECTIVES: To determine whether performance of inserting of pegs and tapping (i) correlates with each other (ii) differentiates between parkinsonian subjects and healthy controls and (iii) reflects severity of Parkinson's disease (PD). SUBJECTS AND METHODS: In 157 previously untreated idiopathic parkinsonian patients and healthy controls, we measured (i) the total time taken to insert 25 pegs from a rack into a series of appropriate holes in a Purdue pegboard-like apparatus and (ii) the number of taps on a contact board with a contact pencil for a period of 32 seconds for assessment of fine motor skills. RESULTS: Results of both tests correlated with each other, differed between parkinsonian subjects and controls and reflected scored severity of PD. Better correlation with intensity of PD was noted with the Purdue pegboard-like task. CONCLUSION: Both tapping and inserting of pegs represent useful tools for objective evaluation of severity of PD. Peg insertion correlated better with disease severity. Both approaches may be useful in future clinical studies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".