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Record W1997375585 · doi:10.3138/ptc.2011-18

The Effect of Previous Loading Cycles on the Reliability of a Clinical Measure of Hamstring Flexibility

2011· article· en· W1997375585 on OpenAlexaffvenue
Arthur Woznowski‐Vu, Richard Preuss

Bibliographic record

VenuePhysiotherapy Canada · 2011
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsCentre de réadaptation Lethbridge-Layton-MackayMcGill University
Fundersnot available
KeywordsMeasure (data warehouse)Flexibility (engineering)HamstringReliability (semiconductor)Computer scienceReliability engineeringPhysical medicine and rehabilitationPhysical therapyMedicineData miningStatisticsMathematicsEngineering

Abstract

fetched live from OpenAlex

PURPOSE: The aim of the study was to determine the effect of successive repetitions of a measure of hamstring flexibility-the passive unilateral straight leg raise (SLR)-on the reliability of this measure. METHOD: Ten repetitions of the SLR were performed on nine healthy adults. Measures were quantified using an electromagnetic tracking system and standardized using a handheld dynamometer by stopping the SLR at a set end-point force. The 10 repetitions were analyzed as two blocks of five, and intra-class correlation coefficients-models (2,1) and (3,k)-were calculated for each block of data. RESULTS: ICC values for both models were comparable between the two blocks of data. CONCLUSION: Previous loading cycles, to a set end-point force, are unlikely to improve the reliability of muscle flexibility assessment in a clinical setting.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.096
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.096
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.347
Teacher spread0.311 · 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 source (direct Gemma or distilled Codex), 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
Published2011
Admission routes2
Has abstractyes

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