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Which trunk inclination directions best predict multidirectional-seated limits of stability among individuals with spinal cord injury?

2012· article· en· W2150189127 on OpenAlexafffund
Cindy Gauthier, Dany H. Gagnon, G. Jacquemin, Cyril Duclos, Kei Masani, Miloš R. Popović

Bibliographic record

VenueJournal of Spinal Cord Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity Health NetworkUniversity of TorontoToronto Rehabilitation InstituteUniversité de MontréalInstitut de Readaptation Gingras Lindsay de Montreal
FundersRéseau Provincial de Recherche en Adaptation-RéadaptationOntario Neurotrauma Foundation
KeywordsTrunkMedicineSpinal cord injuryPhysical medicine and rehabilitationSpinal cord

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine which trunk inclination directions most accurately predict multidirectional-seated limits of stability among individuals with spinal cord injury (SCI). DESIGN: Predictive study using cross-sectional data. SETTING: Pathokinesiology Laboratory. PARTICIPANTS: Twenty-one individuals with complete or incomplete sensorimotor SCI affecting various vertebral levels participated in this study. INTERVENTIONS: Participants were instructed to lean their trunk as far as possible in eight directions, separated by 45° intervals, while seated on an instrumented chair with their feet positioned on force plates. OUTCOMES MEASURES: Eight direction-specific stability indices (DSIs) were used to define an overall stability index (OSI) (limits of stability). RESULTS: All DSIs significantly correlated with the OSI (r = 0.816-0.925). A protocol that only tests the anterior, left postero-lateral, and right lateral trunk inclinations accurately predicts multidirectional-seated postural stability (r(2) = 0.98; P < 0.001). CONCLUSION: Multidirectional-seated postural stability can be predicted almost perfectly by evaluating trunk inclinations performed toward the anterior, left postero-lateral, and right lateral directions.

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.091
GPT teacher head0.417
Teacher spread0.325 · 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.

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

Citations24
Published2012
Admission routes2
Has abstractyes

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