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Record W2052612115 · doi:10.1002/jor.22127

Lower‐limb joint mechanics after total hip arthroplasty during sitting and standing tasks

2012· article· en· W2052612115 on OpenAlexafffund
Mario Lamontagne, Mélanie L. Beaulieu, D. Varin, Paul E. Beaulé

Bibliographic record

VenueJournal of Orthopaedic Research® · 2012
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsSittingJoint (building)Joint arthroplastyTotal hip arthroplastyPhysical medicine and rehabilitationMedicineArthroplastyPhysical therapySurgeryEngineeringStructural engineering

Abstract

fetched live from OpenAlex

While the effect of total hip arthroplasty on the operated limb mechanics is well documented, little is known on its effect on the contralateral limb. The purpose of this study was to measure the joint mechanics of both lower limbs during the tasks of sit-to-stand and stand-to-sit. Twenty total hip arthroplasty patients and 20 control participants performed three trials of each task from which 3D lower-limb joint kinematics and kinetics were obtained. Total hip arthroplasty patients exhibited lower operated-hip joint flexion, extension moments, and power, occurring most frequently near seat-on and seat-off. Despite these reduced kinetic variables in the operated hip, the joints of the non-operated limb generated similar joint kinetics as the matched control participants. These results indicated the patients who underwent total hip arthroplasty could adopt a strategy that allowed them to reduce moments and power generated at the operated lower-limb joints without overcompensating with the non-operated leg. Although such a strategy may be desirable given that higher loads can increase friction and accelerate wear of the prosthesis, reduced loading may be an indication of inadequate muscle strength that needs to be addressed.

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)0.005
metaresearch head score (Gemma)0.001
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 score0.104
Threshold uncertainty score0.800

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.047
GPT teacher head0.320
Teacher spread0.273 · 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

Citations41
Published2012
Admission routes2
Has abstractyes

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