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Record W2115627134 · doi:10.1136/bjsm.2002.002899

Reliability of a device measuring triceps surae muscle fatigability

2004· article· en· W2115627134 on OpenAlexafffund
Michelle Haber, Elan Golan, Laurent Azoulay, Susan R. Kahn, Ian Shrier

Bibliographic record

VenueBritish Journal of Sports Medicine · 2004
Typearticle
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsJewish General Hospital
FundersFaculty of Medicine, McGill UniversityMcGill University
KeywordsMedicineTriceps surae muscleIntraclass correlationHeelPhysical therapyReliability (semiconductor)Physical medicine and rehabilitationBarefootSurgeryAnatomy

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine the test-retest reliability of a protocol using an apparatus designed to standardise the standing heel rise test for the triceps surae muscle. SUBJECTS: 40 healthy subjects volunteered to test short and medium term test-retest reliability (group SM, median age 24 years), and a convenience sample of 38 subjects with a history of unilateral deep vein thrombosis (DVT) volunteered to test long term test-retest reliability (group L, median age 52 years). DESIGN: Subjects carried out 23 heel rises per minute until either the pace or the height could no longer be maintained. Group SM subjects repeated the test 30 minutes later (short term), and again 48 hours later (medium term). Subjects in group L did the test on the unaffected leg, and repeated the test one week later (long term). RESULTS: The median number of heel rises achieved per trial in group SM was 34 (range 16 to 120). The intraclass coefficient (ICC) was 0.93 (SEM 2.1) for both 30 minute and 48 hour test-retest reliability. In group L, the median number of heel rises was 27 (range 9 to 97), with ICC 0.88 and SEM 3.4. CONCLUSIONS: The apparatus is a simple and inexpensive standardised tool that reliably measures triceps surae fatigability in subjects with no current injury. Future research should assess its use in injured patients.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.812
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.264
Teacher spread0.243 · 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

Citations31
Published2004
Admission routes2
Has abstractyes

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