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Comprehensive 3D Architecture of the Adult Human Trapezius: A Cadaveric Study

2019· article· en· W3177429186 on OpenAlexafffundabout
Mikaela L. Stiver, Kaija Käärid, Dinesh Kumbhare, Anne Agur

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicMusicians’ Health and Performance
Canadian institutionsUniversity Health NetworkToronto Rehabilitation InstituteUniversity of Toronto
FundersCanadian Institutes of Health ResearchUniversity Health Network
KeywordsTrapezius muscleCadaveric spasmCadaverMuscle architectureAnatomyConnective tissueMedicinePathologyPhysical medicine and rehabilitationElectromyography

Abstract

fetched live from OpenAlex

Musculoaponeurotic architecture describes the 3D arrangement of the contractile and connective tissue elements within a muscle, or functional partitions thereof. Without a comprehensive appreciation of these features, the certainty with which clinicians may interpret musculoskeletal imaging, assess pathology, and evaluate recovery associated with these muscles is inherently restricted. Current musculoaponeurotic literature is limited by insufficient fibre bundle (FB) sampling, lack of data regarding connective tissue elements, and 2D measurement approaches for 3D parameters. Morphometrically‐complex muscles, like trapezius, are particularly susceptible to overgeneralization using these approaches. A thorough understanding of the contractile and connective tissues of trapezius may help elucidate the etiology and pathophysiology of musculoskeletal disorders associated with this muscle. Accordingly, the primary objective of this study was to capture, quantify, and model the comprehensive 3D musculoaponeurotic architecture of the adult human trapezius muscle from cadaveric data. Ten trapezius muscles from five lightly embalmed cadavers (3F, 2M; ages 64–85 years) were meticulously dissected for this study. Contractile tissue elements were serially digitized in situ at the FB level with a MicroScribe ® G digitizer and modelled in Autodesk ® Maya ® . The surfaces of connective tissue elements (i.e. aponeuroses) were digitized in a grid pattern and integrated into 3D musculoaponeurotic models. Architectural parameters, including FB length, pennation angle, and physiological cross‐sectional area (PCSA), were quantified for the whole muscle and each functional partition (ascending, transverse, and descending). Each trapezius muscle included a minimum of 1000 FBs. Preliminary data analyses reveal consistent patterns of relative mean FB length (ascending = descending > transverse), range of FB lengths (ascending > descending > transverse), and PCSA (transverse > ascending > descending). FBs throughout the muscle volume span between independent medial and lateral aponeuroses, resulting in extensive regions of musculoaponeurotic junction. Aponeuroses are notably substantial in the medial transverse and lateral ascending partitions, while the clavicular attachment of the descending partition had minimal connective tissue present. This study presents a comprehensive 3D model of the adult human trapezius that provides a foundation for improved longitudinal assessment of pathology and continuing clinical education. Future studies will use these data to guide in vivo imaging and functional electromyographic studies investigating musculoskeletal disorders associated with trapezius, such as myofascial pain syndrome. Support or Funding Information Sponsored by a Vanier Canada Graduate Scholarship from CIHR and an Educational Advancement Stipend from the University Health Network. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.021
GPT teacher head0.289
Teacher spread0.269 · 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

Citations1
Published2019
Admission routes3
Has abstractyes

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