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Record W4400514960 · doi:10.1080/1091367x.2024.2377101

Evaluating Test–Retest Reliability and Measurement Error of the Lumbar Flexion Relaxation Ratio Within and Between Days

2024· article· en· W4400514960 on OpenAlexaff
Samuel J. Howarth, Erinn McCreath Frangakis, Steven M. Hirsch, Diana De Carvalho

Bibliographic record

VenueMeasurement in Physical Education and Exercise Science · 2024
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsMemorial University of NewfoundlandUniversity of TorontoUniversity of WaterlooCanadian Memorial Chiropractic College
Fundersnot available
KeywordsReliability (semiconductor)Physical therapyLumbarTest (biology)Physical medicine and rehabilitationLimits of agreementPsychologyStatisticsMedicineMathematicsSurgeryNuclear medicine

Abstract

fetched live from OpenAlex

The flexion relaxation ratio (FRR) of the lumbar extensor muscles is often assessed in experimental and clinical studies. This study evaluated within- and between-session test–retest reliability and measurement error for different FRR formulations. Participants completed two identical data collection sessions 1-week apart. Spine flexion and erector spinae electromyographic data were recorded during two blocks of full forward spine flexion. Participants who numerically self-reported low back pain (≥3/10) during either session were excluded from analysis. Two FRR formulations and their reciprocals were calculated. Generalizability coefficients (GC) was calculated to assess reliability. Standard error of measurement was also determined. Fifty participants were recruited, with six excluded from the analysis. Within-session reliability was moderate-to-good (GC = 0.519–0.791). Between-session reliability was poor-to-moderate (GC = 0.376–0.538). Within-session and between-session measurement errors, respectively, were 38% and 49% of the grand mean FRR. These data suggest limited FRR utility in longitudinal studies.

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)0.005
metaresearch head score (Gemma)0.003
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 score0.865
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.059
GPT teacher head0.366
Teacher spread0.306 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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