Grip force behavior in Gilles de la Tourette syndrome
Bibliographic record
Abstract
We analyzed predictive and reactive grip force behavior in 15 patients with Gilles de la Tourette syndrome (GTS) and 15 sex- and age-matched healthy control subjects. Nine patients were without medication; six patients were on medication. In a first experiment, participants lifted and held instrumented objects of different weight. In a second experiment, participants performed vertical point-to-point and continuous arm movements at different frequencies with a hand-held object. In a third experiment, preparatory and reactive grip force responses to sudden load perturbations were analyzed when a weight was dropped into a hand-held cup either by the subject or unexpectedly by the experimenter. Compared to the healthy subjects, GTS patients had increased grip forces relative to the load force in all tasks. Despite this finding, they adjusted the grip force to changes in load force (due to either a change in the mass lifted or accelerating the mass during continuous movements) in the same way as healthy subjects. The temporal coupling between grip and load force profiles was also similar in patients and healthy controls, and they displayed normal anticipation of impact forces when they dropped a weight into a hand-held cup. We found no significant effect of medication on the performance of GTS patients, regardless of the task performed. These results are consistent with deficient sensory-motor processing in Gilles de la Tourette syndrome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".