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Record W1998669843 · doi:10.1007/s00167-011-1690-y

Measuring the anterior cruciate ligament’s footprints by three‐dimensional magnetic resonance imaging

2011· article· en· W1998669843 on OpenAlexaff
Yung Han, David Kurzencwyg, Adam Hart, Tom Powell, Paul A. Martineau

Bibliographic record

VenueKnee Surgery Sports Traumatology Arthroscopy · 2011
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsMcGill University Health CentreMcGill UniversityMontreal General Hospital
Fundersnot available
KeywordsCadaveric spasmFootprintAnterior cruciate ligamentMedicineMagnetic resonance imagingCadaverNuclear medicineAnatomyRadiologyGeology

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study was to compare 3D MR imaging and open cadaveric measurements of the ACL's footprints to see whether 3D MR imaging measurements are accurate enough to be used for preoperative templating in anatomic ACL reconstruction. METHODS: Eight formalin-injected cadaveric knees were scanned by rapid acquisition isotropic 3D MR imaging. The femoral and tibial footprints were measured on MR imaging and compared with cadaveric dissection. Bland-Altman plots were used to assess the level of agreement. RESULTS: The AM and PL bundles were clearly appreciated in each specimen by 3D MR imaging and cadaveric dissection. The average paired difference in the femoral and tibial footprint measurements was 2, 1, 2, and 2 mm for the femoral footprint length, femoral footprint width, tibial footprint length, and tibial footprint width, respectively. The individual paired measurements were all within the mean difference ± two standard deviations of the difference in the Bland-Altman plot showing strong agreement. CONCLUSION: Measuring the ACL's footprint by 3D MR imaging or open cadaveric dissection has strong agreement and can be used interchangeably. 3D MR imaging has the potential to allow surgeons to: (1) tailor ACL reconstruction technique or graft choice based on ACL footprint size, (2) plan for selective bundle ACL reconstruction for partial tears, and (3) preoperatively template tunnel position according to the patient's individual anatomy.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.189
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.237
Teacher spread0.215 · 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

Citations46
Published2011
Admission routes1
Has abstractyes

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