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Record W2129185561 · doi:10.1016/j.jmpt.2013.05.017

Evaluation of Methods for the Quantification of the Flexion-Relaxation Phenomenon in the Lumbar Erector Spinae Muscles

2013· article· en· W2129185561 on OpenAlexafffund
Alison Schinkel-Ivy, Brian C. Nairn, Janessa D.M. Drake

Bibliographic record

VenueJournal of Manipulative and Physiological Therapeutics · 2013
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaYork University
KeywordsMedicineErector spinae musclesLumbarElectromyographyLow back painTrunkPhysical medicine and rehabilitationSensitivity (control systems)Physical therapyAnatomyPathology

Abstract

fetched live from OpenAlex

OBJECTIVES: There are various methods to quantify the flexion-relaxation phenomenon (FRP); however, there is little standardization. This study aimed to evaluate the performance of various quantification methods in terms of their ability to identify lumbar erector spinae flexion-relaxation during standing forward trunk flexion. METHODS: The study was a cross-sectional design in a laboratory setting. Lumbar erector spinae activation levels were measured in 12 male participants performing full trunk flexion movements. Electromyographical signals were assessed using 16 criteria within 4 quantification methods (visual, statistical, threshold, ratio), and the sensitivity of each was assessed relative to the benchmark criterion (visual inspection of raw electromyography data). RESULTS: Visual inspection and most of the threshold and ratio criteria displayed the highest sensitivity. On average (SD) across the 16 criteria, FRP was positively identified 21.6 (6.2) times of 24 data sets (12 participants, 2 muscles). The visual inspection criteria positively identified FRP in all 24 trials, whereas the statistical method did not identify FRP at all (P = .44 and P = .46 for the left and right sides, respectively). The threshold and ratio criteria positively identified FRP 23.2 (1.5) and 22.5 (3.7) times, on average, respectively. Results from criteria based on differences between upright and fully flexed muscle activation tended to be conservative in FRP identification. The methods were classified as reliable or nonreliable, based on their sensitivity when specific characteristics were evident in the electromyography signals. CONCLUSIONS: Although many of the criteria identified FRP with 100% sensitivity, others produced unrealistic results. The latter may be suitable for other experimental designs or may require reevaluation regarding their ability to identify FRP. Although visual inspection, threshold, or ratio methods performed well and may be appropriate for either biomechanical or clinical research, the threshold method provided the optimal trade-off between performance, consistency, and feasibility for these data.

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)0.017
metaresearch head score (Gemma)0.032
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.032
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.480
GPT teacher head0.478
Teacher spread0.002 · 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

Citations37
Published2013
Admission routes2
Has abstractyes

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