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Record W2346231558

AN ACCURATE THREE-DIMENSIONAL METHOD FOR THE ASSESSMENT OF JOINT LINE SHIFT POST KNEE ARTHROPLASTY

2018· article· en· W2346231558 on OpenAlexaff
Shahram Amiri, Bassam A. Masri, Carolyn Anglin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsJoint (building)OrthodonticsRadiographyKnee JointArthroplastyMedicineImplantSuperimpositionSurgeryComputer scienceArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Accurate comparison of outcomes regarding various surgical options in knee arthroplasty calls for an improved method of joint line analysis that takes into account the preoperative cartilage thickness. Current methods for measuring joint lines have limitations. This is commonly done on anteroposterior or lateral radiographs, by measuring landmarks defining the joint line with reference to a common landmark such as the fibular head, the medial femoral epicondyle or the tibial tuberosity. These radiographic methods are unable to measure important differences between the medial and lateral joint lines. Furthermore, poor accuracies due to sensitivity to patient and X-ray beam positions have been reported for these methods. The aim of this study was to introduce a method to measure the joint line shift for any desired flexion angle of the joint by taking into account the cartilage thickness on both the medial and lateral sides and under weightbearing conditions. The suggested method inludes four steps (Figure 1): a) preoperative joint imaging and 3D-2D matching between pre-operative 3D models and bi-planar 2D images; b) postoperative joint imaging and 3D-2D matching between implant 3D models and 3D models of the bones to bi-planar 2D images; c) superimposition of the locations of the implant components on the preoperative joint positions to determine changes in the joint line on the medial and lateral sides of the joint for both extension and flexion positions. To determine the tibial joint line, the three-dimensional model of the polyethylene inlay was added to the metal tray according to the design of its locking mechanism. Two-dimensional cross-sectional slices of the combined bone and implant models were obtained perpendicular to the tibial tray and passed through the most distal points of the medial and lateral condyles of the femoral component. Joint line shift was measured as the distance between the most distal point on the condyle of the femoral component and the most proximal point on the articular surface of the tibial polyethylene in the direction normal to the mediolateral edge of the tibial tray in the cross-sectional slice. The method was tested on six cadaveric specimens. The joint line shift, measured using the new method, was in the range of −0.2 to 1.3 mm on average (SD=1.3 to 3.8 mm for medial and lateral, flexion and extension). This was significantly different (p≤0.01) from the results of a previously post-op based registration method which did not account for the cartilage thickness. These results demonstrate the importance of the preoperative joint space when analyzing the joint line, and highlight the utility of the newly introduced method. The method introduced in this study combines the preoperative and postoperative images to provide accurate 3D measures of joint line shifts. The method incorporates cartilage thickness in the analysis and is insensitive to patient and X-ray beam position, and can be applied at any flexion angle of the knee. The method yields much smaller measures of joint line shift than a previous approach, which suggests that the previous method may have overestimated joint line shift substantially.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.067
GPT teacher head0.345
Teacher spread0.277 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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