Human Balance in Response to Continuous, Predictable Translations of the Support Base: Integration of Sensory Information, Adaptation to Perturbations, and the Effect of Age, Neuropathy and Parkinson’s Disease
Bibliographic record
Abstract
This short narrative review article moves from early papers that described the behaviour of healthy subjects balancing on a motorized platform continuously translating in the antero-posterior direction. Research from the laboratories of two of the authors and related investigations on dynamic balancing behaviour are briefly summarized. More recent findings challenging time-honoured views are considered, such as the statement that vision plays a head-in-space stabilizing role. The time interval to integrate vision or its withdrawal in the balancing pattern is mentioned as well. Similarities and differences between ageing subjects and patients with peripheral or central disorders are concisely reported. The muscle activities recorded during the translation cycles suggest that vision and amplitude changes of the anticipatory postural activities play a predominant role in controlling dynamic balance during prolonged administration of the predictable perturbation. The potential of this paradigm for rehabilitation of balance problems is discussed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".