The Application of Parallel Robotics to Investigate the Effect of Lumbar Bracing on Trunk Muscle Activity
Bibliographic record
Abstract
Lumbar bracing is prescribed frequently for disability caused by low back pain; however, investigations into this practice demonstrate a range of patient outcomes. This inconsistency may arise from the practice of employing voluntary, single‐axis trunk movements when investigating braces. Alternatively, this study employed a parallel robot to create a standardised, multi‐axis testing environment. Surface electromyographic (sEMG) data were collected from the trunk of 24 asymptomatic participants, who were seated on the robot, tilted to 15°, then circumducted while attempting to maintain an upright posture. Multiple trials were performed for three randomised conditions: non‐braced, soft‐material brace and stiff‐material brace. As expected, the sEMG activity was significantly reduced in the majority of muscle responses (201/240). Unexpectedly, a paradoxical increase in the sEMG activity was observed in 39/240 responses. While lumbar bracing reduces the sEMG activity on average, these data suggest the existence of an infrequent paradoxical response that may provide a possible explanation for the discordant results observed in previous bracing investigations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".