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Record W2498538935 · doi:10.1115/1.4034169

Application of a Novel Measure of In Vivo Knee Joint Laxity

2016· article· en· W2498538935 on OpenAlexafffund
Jessica Küpper, Lindsey Westover, Richard Frayne, Janet L. Ronsky

Bibliographic record

VenueJournal of Biomechanical Engineering · 2016
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsFoothills Medical CentreAlberta Health ServicesUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsReliability (semiconductor)OrthodonticsRepeatabilityMagnetic resonance imagingKnee JointMedicineIn vivoNuclear medicineBiomedical engineeringSurgeryMathematicsRadiologyPhysicsStatistics

Abstract

fetched live from OpenAlex

Current measures of knee joint laxity, such as those found clinically using the KT-2000 arthrometer, are not highly repeatable or reliable by Huber et al. (1997, "Intratester and Intertester Reliability of the KT-1000 Arthrometer in the Assessment of Posterior Laxity of the Knee," Am. J. Sports Med., 25(4), pp. 479-485). In this study, a noninvasive in vivo magnetic resonance (MR) imaging-based measure of laxity, the knee loading apparatus (KLA) with anterior positioning frame, was evaluated with five normal subjects (repeatability study, n = 3). Effects of hormones and muscle guarding were considered. When compared to the KT-2000, the KLA was found to be more precise (±0.33 mm versus ±1.17 mm) but less reliable (Cronbach's alpha > 0.70 in 0/8 versus 5/8 load levels). Improved control of the initial subject position is recommended for future design iterations. The KLA shows promise as an accurate and reliable tool for measuring in vivo joint and ligament laxity.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.585
Threshold uncertainty score0.166

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.242
Teacher spread0.229 · 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 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

Citations9
Published2016
Admission routes2
Has abstractyes

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