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Anatomical variations of the liver and its suspensory system: a cadaver‐based study

2022· article· en· W4225374339 on OpenAlexaff
Beryl Arnould, Pascale Décarie, Gabriel Venne

Bibliographic record

VenueThe FASEB Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsSuspensory ligamentMedicineCadaverAnatomyDissection (medical)LigamentGross anatomyFascia

Abstract

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Introduction & Objective Non‐traumatic musculoskeletal (MSK) pain has become a socio‐economic burden, being the leading cause of absenteeism and prolonged disability. Despite all medical advances, the origin of this pain is still unclear. Studies have reported theories on the role of fascia, nociceptive stimulation, and visceral (im)mobility, and provided evidence for the success of visceral manipulation therapy. The influence of viscera on the MSK system is neglected in scientific research, even though the human body functions best when its components, including the viscera, are free to move in full range. Thus, to understand the role of visceral mobility on the MSK system, this study aimed to investigate the anatomical variation of the liver’s suspensory system via physical measurements and 3D surface scanning. It is hypothesized that anatomical variations will exist. Materials & Methods With IRB clearance, fourteen of twenty‐two formaldehyde‐fixed donors were selected for this study; the other eight donors excluded due to pathology affecting the region of interest. Using gross dissection, livers and diaphragms were isolated. Using a string and a digital micrometer, the length and thickness of the triangular ligaments, the falciform ligament, and the circumference of the bare area was measured for each selected donor. Each isolated liver was weighed. Using a structured light scanner, each liver and suspensory system (bare area and aforementioned ligaments) were digitalized for future analysis. Results The mean lengths (mm) of the left triangular ligament edges were 55.70 ± 27.1, 88.38 ± 31.69, and 63.62 ± 28.76. The mean lengths (mm) of the right triangular ligament edges were 35.32 ± 21.15, 46.98 ± 51.83, and 33.28 ± 21.83. The mean lengths (mm) of the falciform edges were 102.75 ± 30.40, 172.32 ± 35.70, and 105.51 ± 49.25. The mean thicknesses (mm) of the left and right triangular ligaments, and the falciform, were 0.32 ± 0.16, 0.32 ± 0.20, and 0.34 ± 0.31, respectively. The bare area circumference was 443.12 ± 98.38mm, and the mean liver weight was 1439.09 ± 752.39g. Conclusion This dissection‐based study provides evidence of a broad range of anatomical variations that exists between livers and suspensory system, emphasizing the uniqueness of the human body’s anatomy. Significance/Implication This was the first study to examine the morphological variation of the liver’s ligaments and bare area via meticulous physical measurements. The results suggest that the liver’s suspensory system has carefully adapted to each body’s needs and demands. Understanding and visualizing this could help train clinicians who provide visceral manipulation therapy to the liver for MSK pain. A patient’s specific liver morphology may differ greatly from standard anatomy textbook descriptions, so recognizing the anatomical variation of the liver’s suspensory system and the potential impact it has on the MSK system could lead to alternative and improved treatments. Further research could include statistical modeling from the 3D digitalized models. To understand the actual role of the liver’s suspensory system in influencing the MSK system, the biomechanical and proprioceptive properties of the ligaments should be researched.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.036
GPT teacher head0.289
Teacher spread0.253 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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