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Record W2112052685 · doi:10.1080/11762322.2010.512433

The Application of Parallel Robotics to Investigate the Effect of Lumbar Bracing on Trunk Muscle Activity

2010· article· en· W2112052685 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueApplied Bionics and Biomechanics · 2010
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Alberta
FundersMcGill University
KeywordsBracingTrunkRoboticsLumbarComputer sciencePhysical medicine and rehabilitationArtificial intelligenceEngineeringHuman–computer interactionRobotMedicineBraceMechanical engineeringAnatomyBiologyEcology

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.251
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it