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Revision of Well-Fixed Mechanically-Aligned Total Knee Arthroplasty Using Kinematic Alignment for the Restoration of Joint Line Obliquity: Report of a Surgical Technique

2025· article· en· W4410344774 on OpenAlexaff
Brian J. Carlson, David F. Scott

Bibliographic record

VenueJAAOS Global Research and Reviews · 2025
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsObject Research Systems (Canada)
Fundersnot available
KeywordsMedicineKinematicsArthroplastyTotal knee arthroplastyKnee JointSurgeryRadiographyJoint arthroplastyOrthodontics

Abstract

fetched live from OpenAlex

Despite advances in technology and procedures, primary total knee arthroplasty can still result in an unsatisfied patient up to 20% of the time. Many indications for revising total knee arthroplasty have been established, including infection, aseptic loosening, wear, and instability. A newer indication being used in our center is that of a mechanically aligned knee, in which the prosthetic joint line has been substantially altered with respect to the native joint line. In this surgical technique report, we describe a method of revising a painful mechanically aligned total knee arthroplasty using the principles of kinematic alignment, using either the patient's preoperative long-axis radiographs, if available, or those of their contralateral limb, as a guide for the measurement of native joint line obliquity. Although this may be controversial, in our practice, this diagnosis has become an established indication for revision knee arthroplasty, with high success rates and patient satisfaction. In this brief surgical technique report, we present the details of one of our cases.

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)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.464
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.087
GPT teacher head0.417
Teacher spread0.330 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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