Review: Toward a Better Understanding of Coordination in Healthy and Poststroke Gait
Bibliographic record
Abstract
Locomotor coordination characterizes healthy gait and rehabilitation effectiveness in poststroke individuals. However, despite a large number of clinic-based and laboratory-based measurement options, to date there is no gold standard for measurement of locomotor coordination. A lack of a common definition for locomotor coordination may be a cause of this confusion. Coordination during gait includes both spatial and temporal components that may be measured in extrinsic or intrinsic reference frames. Measurement tools have been used to evaluate one or both aspects of coordination. The authors suggest an operational definition of locomotor coordination and describe how current measures in healthy and poststroke individuals fit with this definition. They define locomotor coordination as an ability to maintain a context-dependent and phase-dependent cyclical relationship between different body segments or joints in both spatial and temporal domains. Advantages and disadvantages of laboratory-based measures, such as cyclograms, discrete and continuous relative phase, power spectral density, and others are summarized and discussed. In addition to the definition, the authors propose a clinically feasible measurement paradigm that accentuates the adaptive component of coordination and that may be useful in merging the clinical and laboratory-based approaches to locomotor coordination.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".