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Record W2563057630

THE USE OF POROUS METAL STRUCTURES IN KNEE REVISION: FIX AND FILL

2014· article· en· W2563057630 on OpenAlexaff
David Backstein

Bibliographic record

VenueJournal of Bone and Joint Surgery-british Volume · 2014
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineImpactionFixation (population genetics)TibiaSurgeryResorption
DOInot available

Abstract

fetched live from OpenAlex

Restoration of bone loss is a major challenge of revision TKA surgery. It is critical to achieve of a stable construct to support implants and achieve successful results. Major bone defects of the femoral and/or tibia (AORI type IIB/III) have been reconstructed using impaction grafting, structural allografts or tumor prostheses. The major concerns with structural allograft are graft resorption, mechanical failure, tissue availability, disease transmission, considerable surgical skill required and prolonged operative time. Porous tantalum metaphyseal cones, are becoming the established method of choice to correct large bone defects with several recent studies demonstrating promising results. The high coefficient of friction of these implants provides structural support for femoral and tibial components. The high degree of porosity has excellent potential for bone ingrowth and long-term biologic fixation. Several published series, although with relatively small cohorts of patients, have reported good short-term results with trabecular metal cones for major femoral and tibial bony defects in revision TKA. In a recent study, 16 femoral and 17 tibial cones were reviewed at an average follow up of 33 months (range, 13 to 73 months) the mean Knee Society Score improved from 42 pre-operatively to 83 at last follow up with an improvement of the functional score from an average of 34 to 66 (p

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.675
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.024
GPT teacher head0.226
Teacher spread0.202 · 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 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

Citations0
Published2014
Admission routes1
Has abstractyes

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