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Record W2136226206 · doi:10.1139/h03-013

Relationships Between Lumbar Flexibility, Sit-and-Reach Test, and a Previous History of Low Back Discomfort in Industrial Workers

2003· article· en· W2136226206 on OpenAlexaffabout
Sylvain Grenier, Caryl Russell, Stuart M. McGill

Bibliographic record

VenueCanadian Journal of Applied Physiology · 2003
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Waterloo
FundersHigher Education Research Promotion
KeywordsFlexibility (engineering)Low back painTest (biology)LumbarRange of motionPhysical therapyAsymptomaticMedicineSagittal planeBack painPhysical medicine and rehabilitationPsychologyStatisticsSurgeryMathematics

Abstract

fetched live from OpenAlex

The sit-and-reach (S&R) test is often included in standard fitness tests (e.g., Canadian Physical Activity, Fitness and Lifestyle Appraisal [CPAFLA]), justified on the assumption that it is an indicator of low back health. Two issues were examined here: Is low back flexibility linked to having a history of low back disorders? And is the S&R test an indicator of low back flexibility? The relationship between S&R test scores, lumbar range of motion, and having a history of low back discomfort was examined in 72 asymptomatic (at test time) industrial workers (70 M, 2 F; mean age 35 ys; height 1.79 m; mass 84.7 kg). The S&R test, among many collected, was performed according to the CPAFLA guidelines. History of low back discomfort (LBD) was categorized based on whether or not time was lost from work. The S&R test was unable to distinguish between those with a history of LBD and those without. Specific lumbar sagittal range of motion could make this distinction. A moderate correlation (r = 0.42) surfaced between S&R and lumbar flexibility. This study suggests that the value of S&R as an indicator of previous back discomfort is questionable and there may be better indicators for inclusion in the CPAFLA.

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)0.000
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 score0.017
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.041
GPT teacher head0.252
Teacher spread0.211 · 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

Citations25
Published2003
Admission routes2
Has abstractyes

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