Motor-Equivalent Intersegmental Coordination Is Impaired in Chronic Stroke
Bibliographic record
Abstract
Background. Kinematic abundance permits using different movement patterns for task completion. Individuals poststroke may take advantage of abundance by using compensatory trunk displacement to overcome upper limb (UL) movement deficits. However, movement adaptation in tasks requiring specific intersegment coordination may remain limited. Objective. We tested movement adaptation in both arms of individuals with chronic stroke (n = 16) and nondominant arms of controls (n = 12) using 2 no-vision reaching tasks involving trunk movement (40 trials/arm). Methods. In the “stationary hand task” (SHT), subjects maintained the hand motionless over a target while leaning the trunk forward. In the “reaching hand task” (RHT), subjects reached to the target while leaning forward. For both tasks, trunk movement was unexpectedly blocked in 40% of trials to assess the influence of trunk movement on adaptive arm positioning or reaching. UL sensorimotor impairment, activity, and sitting balance were assessed in the stroke group. The primary outcome measure for SHT was gain ( g), defined as the extent to which trunk displacement contributing to hand motion was offset by appropriate changes in UL movements ( g = 1: complete compensation) and endpoint deviation for RHT. Results. Individuals poststroke had lower gains and greater endpoint deviation using the more-affected compared with less-affected UL and controls. Those with less sensorimotor impairment, greater activity levels, and better sitting balance had higher gains and smaller endpoint deviations. Lower gains were associated with diminished UL adaptability. Conclusions. Tests of condition-specific adaptability of interjoint coordination may be used to measure UL adaptability and changes in adaptability with treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".