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Record W2339795579 · doi:10.1123/jab.2014-0197

Biomechanical and Electromyographic Comparisons of Isometric Trunk Flexor Endurance Test Postures: Prone Plank Versus V-Sit

2015· article· en· W2339795579 on OpenAlexafffund
Lindsay L. Musalem, Tatjana Stankovic, Drazen Glisic, Gillian E. Cook, Tyson A.C. Beach

Bibliographic record

VenueJournal of Applied Biomechanics · 2015
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsIsometric exercisePlankTrunkLumbarMedicineErector spinae musclesCore stabilityBack musclesElectromyographyPhysical medicine and rehabilitationLumbar spinePhysical therapyAnatomySurgery

Abstract

fetched live from OpenAlex

The objective of this study was to investigate why holding times on 2 different tests of isometric trunk flexor endurance capacity (prone plank and v-sit) are weakly correlated. Body position and ground reaction force data from 10 men and 10 women were used to conduct static biomechanical analyses of both test postures, and bilateral activations of the rectus abdominis, internal and external obliques, latissimus dorsi, and lumbar and thoracic erector spinae were measured in a second sample of 15 men and 15 women while holding the test postures. No between-posture differences in net low back flexor moments were found (P = .111), but the lumbar spine was 28° more flexed in the v-sit than in the plank (P < .001). No between-posture differences were detected in the rectus abdominis (P = .397), external obliques (P = .204), internal obliques (P = .226), or lumbar erector spinae (P = .116) activation levels, but those of the thoracic erector spinae (P = .0253) and latissimus dorsi (P < .001) were greater in the plank than in the v-sit. Altogether, the findings suggest that differences between plank and v-sit holding times are most likely related to between-test differences in lumbar spine postures and shoulder demands.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
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 head0.026
GPT teacher head0.284
Teacher spread0.258 · 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 designBench or experimental
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

Citations9
Published2015
Admission routes2
Has abstractyes

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