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Record W3023285104 · doi:10.1177/2325967120915709

Femoral Tunnel Placement Analysis in ACL Reconstruction Through Use of a Novel 3-Dimensional Reference With Biplanar Stereoradiographic Imaging

2020· article· en· W3023285104 on OpenAlexaff
Julien Montreuil, Joseph Saleh, Thierry Cresson, Jacques A. de Guise, Frédéric Lavoie

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsCentre Hospitalier de l’Université de MontréalUniversité de SherbrookeÉcole de Technologie SupérieureMontreal General Hospital
Fundersnot available
KeywordsMedicineAnterior cruciate ligament reconstructionIntraclass correlationFootprintReproducibilityAnterior cruciate ligamentQuadrant (abdomen)FEMORAL CONDYLEOrthodonticsBiomedical engineeringNuclear medicineSurgeryAnatomyCartilageMathematicsGeology

Abstract

fetched live from OpenAlex

Background: The femoral-sided anatomic footprint of the anterior cruciate ligament (ACL) has been widely studied during the past decades. Nonanatomic placement is an important cause of ACL reconstruction (ACLR) failure. Purpose: To describe femoral tunnel placement in ACLR through use of a comprehensive 3-dimensional (3D) cylindrical coordinate system combining both the traditional clockface technique and the quadrant method. Our objective was to validate this technique and evaluate its reproducibility. Study Design: Descriptive laboratory study. Methods: The EOS Imaging System was used to make 3D models of the knee for 37 patients who had undergone ACLR. We designed an automated cylindrical reference software program individualized to the distal femoral morphology of each patient. Cylinder parameters were collected from 2 observers’ series of 3D models. Each independent observer also manually measured the corresponding parameters using a lateral view of the 3D contours and a 2-dimensional stereoradiographic image for the corresponding patient. Results: The average cylinder produced from the first observer’s EOS 3D models had a 30.0° orientation (95% CI, 28.4°-31.5°), 40.4 mm length (95% CI, 39.3-41.4 mm), and 19.3 mm diameter (95% CI, 18.6-20.0 mm). For the second observer, these measurements were 29.7° (95% CI, 28.1°-31.3°), 40.7 mm (95% CI, 39.7-41.8 mm), and 19.7 mm (95% CI, 18.8-20.6 mm), respectively. Our method showed moderate intertest intraclass correlation among all 3 measuring techniques for both length ( r = 0.68) and diameter ( r = 0.63) but poor correlation for orientation ( r = 0.44). In terms of interobserver reproducibility of the automated EOS 3D method, similar results were obtained: moderate to excellent correlations for length ( r = 0.95; P < .001) and diameter ( r = 0.66; P < .001) but poor correlation for orientation ( r = 0.29; P < .08). With this reference system, we were able to describe the placement of each individual femoral tunnel aperture, averaging a difference of less than 10 mm from the historical anatomic description by Bernard et al. Conclusion: This novel 3D cylindrical coordinate system using biplanar, stereoradiographic, low-irradiation imaging showed a precision comparable with standard manual measurements for ACLR femoral tunnel placement. Our results also suggest that automated cylinders issued from EOS 3D models show adequate accuracy and reproducibility. Clinical Relevance: This technique will open multiple possibilities in ACLR femoral tunnel placement in terms of preoperative planning, postoperative feedback, and even intraoperative guidance with augmented reality.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.038
GPT teacher head0.273
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), 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".

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Citations6
Published2020
Admission routes1
Has abstractyes

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