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Record W2417422603 · doi:10.1177/107110070002100511

The Effect of Axial Load on the In Vivo Anterior Drawer Test of the Ankle Joint Complex

2000· article· en· W2417422603 on OpenAlexaff
Wen Liu, Murray E. Maitland, Benno M. Nigg

Bibliographic record

VenueFoot & Ankle International · 2000
Typearticle
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAnkleMedicineFlexibility (engineering)Joint (building)Displacement (psychology)OrthodonticsBiomechanicsJoint stabilityInstabilityStructural engineeringAnatomyMechanicsMathematics

Abstract

fetched live from OpenAlex

The anterior drawer test is commonly used in the diagnosis of ankle joint mechanical instability. However, the effect of axial load on the anterior drawer test has not been examined in vivo. The purpose of the study was to assess the effect of axial load on passive anterior instability, and on the diagnostic measurement of the anterior drawer instability of the ankle joint complex. A total of 21 subjects with various degrees of ankle sprains were tested on a device that could continuously record applied anterior force and the resultant displacement of the rear-foot. Anterior drawer flexibility of the ankle joint complex in a neutral dorsi/plantar flexion position was quantified on both feet for all subjects without and with an axial load (385 N). Flexibility of the ankle joint complex in anterior drawer was defined as the slope of a linear load-displacement curve (which fitted test data with high correlation coefficients (r>0.991)). With axial load, anterior drawer flexibility was significantly reduced by 28.8% compared to that without axial load. The difference in anterior drawer flexibility between injured and intact ankles significantly decreased with axial load. An axial load increased the stability of ankle joint complex. However, axial load reduced the sensitivity of anterior drawer test to mechanical instability of the ankle joint complex.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0030.000

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 head0.016
GPT teacher head0.264
Teacher spread0.247 · 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.

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

Citations15
Published2000
Admission routes1
Has abstractyes

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