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Record W2086881159 · doi:10.1016/j.orthres.2003.10.017

Instrumented measurement of in vivo anterior—posterior translation in the canine knee to assess anterior cruciate integrity

2004· article· en· W2086881159 on OpenAlexfundno aff
Mandi J. Lopez, William W. Hagquist, Susan Jeffrey, Sara Gilbertson, Mark D. Markel

Bibliographic record

VenueJournal of Orthopaedic Research® · 2004
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsnot available
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesMcMaster UniversityNational Institutes of HealthSmith and Nephew
KeywordsAnterior cruciate ligamentMedicineTibiaOrthodonticsNuclear medicineAnatomy

Abstract

fetched live from OpenAlex

This study was designed to objectively quantify in vivo anterior-posterior canine knee translation relative to anterior cruciate ligament (ACL) integrity. Tibial translation was determined in one knee of 43 crossbreed hounds from radiographs performed while a set anterior and then posterior force was applied to the tibia using a custom designed device. The total (TTT), anterior (ATT), and posterior (PTT) tibial translation were measured (absolute) and normalized to the width of the tibia (normalized). Absolute and normalized TTT was significantly greater in ruptured ACL knees than in partially disrupted (PD) ACL knees, which were significantly greater than in intact ACL knees. ATT and PTT was significantly greater in ruptured ACL knees than in PD or intact ACL knees, which were not significantly different. The sensitivity and specificity of normalized TTT to distinguish knees with intact from PD ACLs were both 100%. Normalized TTT to distinguish knees with PD from ruptured ACLs had a sensitivity and specificity of 100% and 92%, respectively. Intra- and inter-observer intra-class correlation coefficients were 0.84 or higher for all translations. This precise non-invasive technique to assess canine knee translational stability and ACL integrity permits repetitive, objective measurements for diagnostic use and to assess therapeutic intervention efficacy.

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)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.121
GPT teacher head0.394
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

Citations25
Published2004
Admission routes1
Has abstractyes

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