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Record W2061628598 · doi:10.3109/09638288.2012.690497

Predicting levels of basic functional mobility, as assessed by the Timed “Up and Go” test, for individuals with stroke: discriminant analyses

2012· article· en· W2061628598 on OpenAlexafffund
Christina Danielli Coelho de Morais Faria, Luci Fuscaldi Teixeira‐Salmela, Sylvie Nadeau

Bibliographic record

VenueDisability and Rehabilitation · 2012
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsInstitut de Readaptation Gingras Lindsay de MontrealUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsGaitStroke (engine)Analysis of varianceTimed Up and Go testChronic strokePhysical medicine and rehabilitationPhysical therapyMedicineLinear discriminant analysisTest (biology)PsychologyRehabilitationBalance (ability)MathematicsInternal medicineStatistics

Abstract

fetched live from OpenAlex

PURPOSE: To compare stroke subjects with different levels of functional mobility, as determined by the Timed "Up and Go" (TUG) test, with controls and outline which clinical functional measures could be combined to predict functional mobility. METHOD: Twenty-two chronic stroke (54.7 ± 15.4 years) and 22 healthy subjects (54.7 ± 15.4 years) performed the TUG and were assessed regarding the paretic or non-dominant quadriceps strength, maximal gait speed, and quality of life (QL). Each group was divided into fast, intermediate, and slow sub-groups regarding their TUG performances. ANOVAs were employed to investigate the main and interaction effects between the groups and sub-groups and discriminant analyses to predict group membership. RESULTS: For both groups, the three sub-groups were significantly different regarding their TUG scores (26.21 < F < 32.73; p < 0.006). The significant interactions indicated that faster stroke subjects demonstrated similar TUG scores, compared to those of all the healthy sub-groups. Maximal gait speed and QL showed significant discriminant functions and correctly classified 86.4% of the original grouped cases. CONCLUSIONS: Fast stroke subjects demonstrated similar TUG performances compared to those of healthy subjects. Group membership was correctly classified for the majority of subjects, except for the fast stroke sub-group, but only for the variables related to gait speed and QL.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.009
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.062
GPT teacher head0.396
Teacher spread0.334 · 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

Citations45
Published2012
Admission routes2
Has abstractyes

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