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Record W1514889211 · doi:10.1002/jor.22529

Decreased posterior cruciate and altered collateral ligament loading following ACL transection: A longitudinal study in the ovine model

2013· article· en· W1514889211 on OpenAlexafffund
Mohammad Atarod, Cyril B. Frank, Nigel G. Shrive

Bibliographic record

VenueJournal of Orthopaedic Research® · 2013
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsAlberta Bone and Joint Health InstituteUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsAnterior cruciate ligamentGaitMedicineStifle jointACL injuryKnee JointCruciate ligamentHoofGait analysisMedial collateral ligamentLigamentBiomechanicsAnatomyPhysical medicine and rehabilitationSurgery

Abstract

fetched live from OpenAlex

Although ACL deficiency is shown to lead to joint degeneration, few quantitative data are reported on its effect on soft tissue structures surrounding the knee joint, specifically, the posterior cruciate and collateral ligaments. The kinematics of the stifle joint of sheep (N = 5) were measured during "normal" gait, as well as 4 and 20 weeks after ACL transection. These motions were reproduced using a unique robotic manipulator and the loads borne by PCL, MCL, and LCL during gait were determined. Our results demonstrated a significant decrease in mean PCL loads 20 weeks post-ACL injury, at hoof-strike (0% of gait, p = 0.034), hoof-off (66% of gait, p = 0.006), peak-swing (85% of gait, p = 0.026), and extension-before-hoof-strike (95% of gait, p = 0.028). Mean MCL loads did not significantly increase following ACL transection, maybe due to large between-animal variation. Finally, mean LCL loads indicated a significant decrease (p < 0.047) at 20 weeks across the entire gait cycle. From a clinical perspective, the load redistributions observed in cruciate and collateral ligaments following ACL injury indicate that these tissues can carry/adapt to the altered mechanical environment of the joint. The considerable variability in the magnitudes of change following ACL injury among animals also simulates clinical variability in humans after trauma.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.081
GPT teacher head0.390
Teacher spread0.310 · 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".

Quick stats

Citations36
Published2013
Admission routes2
Has abstractyes

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