Compensation for balance disorders: analysis of this multifactorial process
Bibliographic record
Abstract
The European Society for Clinical Evaluation of Balance Disorders (ESCEBD), based in Nancy, France, has been meeting yearly since 2005 to discuss equilibrium-related themes that are not yet clearly defined or standardized. One of our latest discussions was with regard to outlining strategies of internal and external compensation that may be used to cope with balance disorders. A Committee was elected to discuss the mechanisms of compensation that may be involved in coping with balance system disorders. Compensation, referring to the immediate or short-term adaptive mechanisms that are used to counterbalance the effects of deficiencies that disrupt balance maintenance, can include alternative strategies, resources, or environmental supports to overcome deficits or challenges associated with a deficiency. The strategies can be internal (i.e. utilizing the individual's own multi-sensory neural integration, motor, and cognitive resources) or external (i.e. modifying the environment, or using assistive or adaptive devices) to reduce fall hazard and enhance safety. This report focuses principally on internal compensation, generated by sensorimotor processes of the central nervous system (CNS) in response to impairment of either sensory information (e.g. vestibular pathologies), the musculoskeletal system (e.g. lower limb amputation and myopathies) or the CNS itself (e.g. upper motor neuron syndrome). The multifactorial process of compensation may explain the limitations encountered by the CNS in compensating for complex bodily impairments and may also limit our understanding of how the CNS adapts to balance disorders. Newly developed devices, such as wearable sensory substitution devices, are on the horizon as possible tools.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".