Compensation Strategies for Gait Impairments in Parkinson Disease: A Review
Bibliographic record
Abstract
Importance:Patients with Parkinson disease can use a wide variety of strategies to compensate for their gait impairments. Examples include walking while rhythmically bouncing a ball, crossing the legs when walking, or stepping over an inverted cane. An overview and classification of the many available compensation strategies may contribute to understanding their underlying mechanisms and developing focused rehabilitation techniques. Moreover, a comprehensive summary of compensation strategies may help patients by allowing them to select a strategy that best matches their needs and preferences and health care professionals by permitting them to incorporate these into their therapeutic arsenal. To create this overview, this narrative review discusses collected video recordings of patients who spontaneously informed clinicians about the use of self-invented tricks and aids to improve their mobility. Observations:Fifty-nine unique compensation strategies were identified from approximately several hundred videos. Here, these observed strategies are classified into 7 main categories for elaboration on their possible underlying mechanisms. The overarching working mechanisms involve an allocation of attention to gait, the introduction of goal directedness, and the use of motor programs that are less automatized than those used for normal walking. Conclusions and Relevance:Overall, these compensation strategies seem to appeal to processes that refer to earlier phases of the motor learning process rather than to a reliance on final consolidation. This review discusses the implications of the various compensation strategies for the management of gait impairment in Parkinson disease. The publisher's final version of this work can be found at https://doi.org/10.1001/jamaneurol.2019.0033. Deposited by shareyourpaper.org and openaccessbutton.org. We've taken reasonable steps to ensure this content doesn't violate copyright. However, if you think it does you can request a takedown by emailing help@openaccessbutton.org.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".