Postural orientation with conflicting visual and graviceptive cues to ‘upright’ among individuals with and without a history of post-stroke ‘pushing’
Bibliographic record
Abstract
Purpose: This study aimed to determine how people with stroke, with and without pushing behaviour, use sensory cues to control postural orientation. Methods: Eight people with chronic stroke (4 with history of pushing behaviour), 5 people with sub-acute stroke (1 with active pushing behaviour) and 8 similarly-aged controls with no history of stroke participated. Participants sat in a motion platform while viewing a 240-degree screen upon which a city street scene was projected. Postural orientation (shoulder and trunk angles) was measured relative to the direction of gravity during 6 trials: visual scene tilted 18-degrees left and right; motion base tilted 18-degrees left and right; and both visual scene and motion base tilted 18-degrees left and right. Results: Participants with stroke did not appear to adjust their posture in response to visual scene tilt to a greater extent than control participants. For most conditions, chronic stroke participants with a history of pushing behaviour oriented their posture more towards the contralesional side than controls. When the motion base was tilted, sub-acute participants with no evidence of pushing behaviour oriented their posture more in the direction of motion base tilt than controls (e.g., when the motion base tilted to their ipsilesional sides, their trunks and shoulders were oriented to the ipsilesional side). Conclusion: This study did not find evidence that people with stroke with and without a history of pushing behaviour rely more on static visual cues to control postural orientation than people without stroke.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".