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Individualizing a Total Knee Arthroplasty with Three-Dimensional Planning

2024· article· en· W4392739060 on OpenAlexaff
H. Gene Dossett, David G. Deckey, Henry D. Clarke, Mark J. Spangehl

Bibliographic record

VenueJAAOS Global Research and Reviews · 2024
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsObject Research Systems (Canada)
FundersAustralian Government
KeywordsTotal knee arthroplastyPlan (archaeology)MedicineIdeal (ethics)Radiation treatment planningArthroplastyPhysical therapyComputer scienceMedical physicsSurgery

Abstract

fetched live from OpenAlex

Total knee arthroplasty (TKA) is evolving from mechanical alignment to more individualized alignment options in an attempt to improve patient satisfaction. Thirteen-year survival of kinematically aligned prostheses has recently been shown to be similar to mechanically aligned TKA, allaying concerns of long-term failure of this newer individualized technique. There is a complex inter-relationship of three-dimensional knee and limb alignment for a TKA. This article will review planning parameters necessary to individualize each knee, along with a discussion of how these parameters are related in three dimensions. Future use of computer software and machine learning has the potential to identify the ideal surgical plan for each patient. In the meantime, the material presented here can assist surgeons as newer individual alignment planning becomes a 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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.658
Threshold uncertainty score0.749

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.091
GPT teacher head0.399
Teacher spread0.308 · 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 designNot applicable
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

Citations8
Published2024
Admission routes1
Has abstractyes

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