MétaCan
Menu
Back to cohort
Record W2922462879 · doi:10.1242/jeb.188292

Contribution of individual quadriceps muscles to knee joint mechanics

2019· article· en· W2922462879 on OpenAlexafffund
Seong-won Han, Andrew Sawatsky, Heiliane de Brito Fontana, Walter Herzog

Bibliographic record

VenueJournal of Experimental Biology · 2019
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversity of CalgaryKillam TrustsFaculty of Veterinary Medicine, University of CalgaryCanada Research Chairs
KeywordsAgonistic behaviourVastus medialisKnee JointMuscle groupPhysical medicine and rehabilitationAnatomyMedicineElectromyographySurgery

Abstract

fetched live from OpenAlex

Many attempts have been made to determine the contribution of individual muscles in an agonistic group to the mechanics of joints. However, previous approaches had the limitations that muscles often could not be controlled in a precise manner, that individual muscles in an agonistic group could not be activated individually, and that individual muscle contributions could not be measured in an actively contracting agonistic group. Here, we introduce a surgical approach that allows for controlled activation of individual muscles of an agonistic group. The approach is illustrated for the vastus lateralis (VL), vastus medialis (VM) and rectus femoris (RF) of the rabbit quadriceps femoris group. We provide exemplar results for potential applications of the approach, such as measuring the pressure distribution in the patellofemoral joint, and the torque-angle relationship of VL, VM and RF when activated individually and when the three muscles are activated simultaneously.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.014
GPT teacher head0.252
Teacher spread0.237 · 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 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

Citations18
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Experimental BiologySame topicMuscle activation and electromyography studiesFrench-language works237,207