Variation in planar area movements in healthy individuals: Influences of hand dominance and task type (tool use vs. pantomime)
Bibliographic record
Abstract
Independent living requires manipulating tools, such as using a knife to slice a piece of bread. However, people with apraxia (~60% of left hemisphere stroke patients) are unable to use tools and objects in daily life activities. The Waterloo-Sunnybrook Apraxia Battery assesses patients' ability to perform ADL gestures. To create normative profiles for comparisons, 10 healthy right-handed participants both pantomimed (pretended) tool use and used physical tools for transitive gestures from the Apraxia Battery bimanually. Hand positions were optoelectronically recorded for 3 trials. For the gesture of "hammering to pound a nail", the planar range covered by the preferred hand (range YZ [right object] = 33.10mm, 137.30mm; range YZ [right pant] = 270.22mm, 378.32mm) was smaller than that of the non-preferred hand (range YZ [left object] = 79.59mm, 217.64mm; range YZ [left pant] = 394.69mm, 428.30mm). Further, there was more controlled movement during actual tool use than pantomime (Figure 1), exhibited by a smaller range in position (range YZ [right object] = 33.10mm, 137.30mm; range YZ [left object] = 79.59mm, 217.64mm). Pantomiming produced a greater trajectory of movement (range YZ [right pant] = 270.22mm, 378.32mm; range YZ [left pant] = 394.69mm, 428.30mm). These trends are consistent across the subject pool. These findings suggest that considering variation in healthy persons will help to identify differing levels of variability in persons with apraxia.Acknowledgments: NSERC and the Heart and Stroke Foundation of Ontario
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".