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Record W2345137467 · doi:10.4103/0976-3147.165412

Reliability and sensitivity to change of the timed standing balance test in children with down syndrome

2015· article· en· W2345137467 on OpenAlexaff
Vencita Priyanka Aranha, Asir John Samuel, Shikha Saxena

Bibliographic record

VenueJournal of Neurosciences in Rural Practice · 2015
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineReliability (semiconductor)Balance (ability)Sensitivity (control systems)Test (biology)Balance testPhysical medicine and rehabilitationReliability engineeringPediatrics

Abstract

fetched live from OpenAlex

OBJECTIVE: To estimate the reliability and sensitivity to change of the timed standing balance test in children with Down syndrome (DS). METHODS: It was a nonblinded, comparison study with a convenience sample of subjects consisting of children with DS (n = 9) aged 8-17 years. The main outcome measure was standing balance which was assessed using timed standing balance test, the time required to maintain in four conditions, eyes open static, eyes closed static, eyes open dynamic, and eyes closed dynamic. RESULTS: Relative reliability was excellent for all four conditions with an Interclass Correlation Coefficient (ICC) ranging from 0.91 to 0.93. The variation between repeated measurements for each condition was minimal with standard error of measurement (SEM) of 0.21-0.59 s, suggestive of excellent absolute reliability. The sensitivity to change as measured by smallest real change (SRC) was 1.27 s for eyes open static, 1.63 s for eyes closed static, 0.58 s for eyes open dynamic, and 0.61 s for eyes closed static. CONCLUSIONS: Timed standing balance test is an easy to administer test and sensitive to change with strong absolute and relative reliabilities, an important first step in establishing its utility as a clinical balance measure in children with DS.

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)0.026
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.058
GPT teacher head0.354
Teacher spread0.296 · 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

Citations14
Published2015
Admission routes1
Has abstractyes

Explore more

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