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Record W2018969291 · doi:10.1002/jor.21139

Precise landmarking in computer assisted total knee arthroplasty is critical to final alignment

2010· article· en· W2018969291 on OpenAlexaff
Yaron S. Brin, Isaac Livshetz, John Antoniou, Sari Greenberg‐Dotan, David J. Zukor

Bibliographic record

VenueJournal of Orthopaedic Research® · 2010
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsCoronal planeTotal knee arthroplastyComputer scienceTibiaComputer-assisted surgeryNavigation systemTotal knee replacementFrame (networking)OrthodonticsComputer visionArtificial intelligenceMedicineSurgeryAnatomy

Abstract

fetched live from OpenAlex

Image-free computer navigation systems build a frame of reference of a patient's knee from anatomical landmarks entered by the surgeon during the initial stage of total knee arthroplasty. We performed tibial cuts on 70 sawbones using computer navigation. All landmarks were marked identically except for the tibial mechanical entry point, which was marked correctly in 10 bones and with offsets of 5, 10, and 15 mm medially and laterally in the others. The actual coronal angle of the tibial cuts was measured directly and compared to the final angle given by the navigation system. Significant deviations of the coronal angle were observed in the trial groups. Landmarking errors during navigated TKA can lead to inaccurate tibial bone cuts. This navigation system did not have an iterative software method to verify landmarking errors that can lead to inaccurate tibia bone cuts.

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)0.005
metaresearch head score (Gemma)0.020
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.059
GPT teacher head0.377
Teacher spread0.319 · 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".

Quick stats

Citations20
Published2010
Admission routes1
Has abstractyes

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