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Record W2040243369 · doi:10.1002/jor.20621

The human anterior cruciate ligament: Sex differences in ultrastructure and correlation with biomechanical properties

2008· article· en· W2040243369 on OpenAlexaff
Javad Hashemi, Naveen Chandrashekar, H. Mansouri, James R. Slauterbeck, Daniel M. Hardy

Bibliographic record

VenueJournal of Orthopaedic Research® · 2008
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsUniversity of Waterloo
FundersOrthopedic Research and Education FoundationTexas Tech University
KeywordsCadaveric spasmAnterior cruciate ligamentUltrastructureCollagen fibrilAnatomyFibrilLigamentMedicineChemistry

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate the existence of sex-based differences in the ultrastructural characteristics of the human anterior cruciate ligament (ACL) as the underlying cause of differences in the structural and mechanical properties between sexes. The ACL of six male and six female cadaveric donors were randomly chosen from a pool of 10 male and 10 female ACLs that had previously been tested for their structural and mechanical properties. Eighteen tissue samples from the distal, proximal, and middle sections of the anteromedial and posterolateral bundles were analyzed by transmission electron microscopy. Female ACLs exhibited both lower fibril concentration and lower percent area occupied by collagen fibrils (p < 0.05) compared to males. There was also a difference in the fibril diameters (p < 0.05); donor age, height, body mass, and body mass index contributed significantly to this difference. In females, ACL stiffness and modulus of elasticity were highly correlated to fibril concentration (r = 0.96 and 0.97, respectively); in males ACL failure load and strength were highly correlated to percent area occupied by collagen (r = 0.96 and 0.96, respectively). These differences in ultrastructure may underlie differences in ACL properties between sexes.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.052
GPT teacher head0.322
Teacher spread0.270 · 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 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

Citations85
Published2008
Admission routes1
Has abstractyes

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