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Dynamic trunk stability is improved in paraplegics following kayak ergometer training

2007· article· en· W1480254891 on OpenAlexaff
Anna Bjerkefors, Mark G. Carpenter, Alf Thorstensson

Bibliographic record

VenueScandinavian Journal of Medicine and Science in Sports · 2007
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of British Columbia
FundersNorrbacka-Eugeniastiftelsen
KeywordsTrunkKinematicsPhysical medicine and rehabilitationSittingBalance (ability)Linear accelerationPhysical therapyMedicineSpinal cord injuryCycle ergometerBalance trainingAccelerationSimulationComputer scienceSpinal cordPhysicsHeart rate

Abstract

fetched live from OpenAlex

The purpose of the study was to assess whether postural stability in persons with spinal cord injury (SCI) could be affected by training. Ten post-rehabilitated persons with thoracic SCI performed 30 sessions of kayak ergometer training during a 10-week period. The ergometer was modified with a balance module adjustable in the medio-lateral direction. Before and after the training period, horizontal support-surface translations were presented randomly, either in the forward or backward direction, or to the side, while subjects sat in their own wheelchairs. The platform perturbation consisted of an unpredictable initial acceleration, followed by a constant-velocity phase and a predictable deceleration. Markers were applied on the trunk and movement data were recorded in 3D. Four kinematic responses of trunk angular and linear displacement were investigated. In general, postural stability was improved after training with smaller rotational and linear displacements of the trunk observed during both predictable and unpredictable translations in all directions. Thus, the training was able to improve the ability of persons with long-standing SCI to maintain an upright sitting posture in response to externally generated balance perturbations, which should imply an increased capacity to master similar challenges to balance in everyday life.

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 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)0.013
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.065
GPT teacher head0.399
Teacher spread0.333 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations52
Published2007
Admission routes1
Has abstractyes

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