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Record W2007350562 · doi:10.1080/17453670510041736

Limited influence of prosthetic position on aseptic loosening of elbow replacements

2005· article· en· W2007350562 on OpenAlexaff
J. C. T. van der Lugt, Ronald B. Geskus, PIET M. ROZING

Bibliographic record

VenueActa Orthopaedica · 2005
Typearticle
Languageen
FieldMedicine
TopicElbow and Forearm Trauma Treatment
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsMedicineRadiodensityElbowRadiographyProsthesisAseptic processingCondyleSurgeryOrthodonticsOrthopedic surgeryDentistry

Abstract

fetched live from OpenAlex

BACKGROUND: Aseptic loosening of elbow replacements, seen in long-term follow-up, remains a problem. In this study, we attempted to determine the influence of cementing technique, prosthetic position, different component sizes, use of a bone plug, and intraoperative fractures on the development and progression of radiolucent lines and aseptic loosening. METHODS: We studied standard radiographs of 125 primary Souter-Strathclyde total elbow prostheses using the Wrightington method. Additionally, 104 preoperative radiographs were available for analysis. We used a Markow statistical model to detect relationships between all factors described above. RESULTS: After a mean follow-up time of 5.5 (2-19) years, 21 (17%) prostheses had loosened radiographically (10-year survival: 65%). When the humeral component was tilted more medially or more anteriorly, we found development of radiolucent lines at the medial condyle and at the posterior side of the humeral component. However, the progression of these lines was not influenced by these positions. No other prognostic factors for radiolucent lines or aseptic loosening were found. INTERPRETATION: Despite the small number of elbows studied, the weak influence of prosthetic position on aseptic loosening gives more ground for a multifactorial cause for aseptic loosening of the Souter-Strathclyde total elbow prosthesis.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.010
GPT teacher head0.262
Teacher spread0.252 · 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

Citations17
Published2005
Admission routes1
Has abstractyes

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