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Record W2564737679 · doi:10.1080/00222895.2016.1241748

Trunk Compensation During Bimanual Reaching at Different Heights by Healthy and Hemiparetic Adults

2016· article· en· W2564737679 on OpenAlexafffund
Bulmaro A. Valdés, Stephanie Glegg, H. F. Machiel Van der Loos

Bibliographic record

VenueJournal of Motor Behavior · 2016
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsSunny Hill Health Centre for ChildrenUniversity of British Columbia
FundersUniversity of British ColumbiaHeart and Stroke Foundation of Canada
KeywordsTrunkPhysical medicine and rehabilitationPsychologyDisplacement (psychology)MedicinePhysical therapyBiology

Abstract

fetched live from OpenAlex

The authors explored how trunk compensation and hand symmetry in stroke survivors and healthy controls were affected by the distance and height of virtual targets during a bimanual reaching task. Participants were asked to reach to 4 different virtual targets set at: 90% of their arm length at shoulder, xiphoid process, and knee height, and 50% of their arm length at xiphoid process height. For the stroke group, for all targets, the hands' movements were more asymmetrical than those of the healthy group, with more asymmetry observed in the direction of gravity, and trunk forward displacement values were larger and more variable. The knee targets had the largest trunk displacement values; index of curvature and trunk displacement were strongly correlated with participants' impairment scores. A strong correlation was found between the hands' asymmetry in the anterior or posterior direction for the shoulder targets, and the impairment scores. The results suggest that target height influences the degree of trunk compensation and hand symmetry during bimanual reaching by hemiparetic participants.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.269
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), 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

Citations27
Published2016
Admission routes2
Has abstractyes

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