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Record W2017119046 · doi:10.1503/cjs.000913

Dislocation after the first and multiple revision total hip arthroplasty: comparison between acetabulum-only, femur-only and both component revision hip arthroplasty

2014· article· en· W2017119046 on OpenAlexaffvenue
Yona Kosashvili, Michael Drexler, David Backstein, Oleg Safir, Dror Lakstein, Alex Safir, Raja Chakravertty, Tim Dwyer, Allan E. Gross

Bibliographic record

VenueCanadian Journal of Surgery · 2014
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsWomen's College HospitalMount Sinai Hospital
Fundersnot available
KeywordsAcetabulumMedicineFemurTotal hip arthroplastyArthroplastyDislocationSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Dislocation may complicate revision total hip arthroplasty (THA). We examined the correlation between the components revised during hip arthroplasty (femur only, acetabulum only and both components) to the rates of dislocation in the first and multiple revision THA. METHODS: We obtained data from consecutive revision THAs performed between January 1982 and December 2005. Patients were grouped into femur-only revision, acetabulum-only revision and revision THA for both components. RESULTS: A total of 749 revision THAs performed during the study period met our inclusion criteria: 369 first-time revisions and 380 repeated revisions. Dislocation rates in patients undergoing first-time revisions (5.69%) were significantly lower than in those undergoing repeated revisions (10.47%; p = 0.022). Within the group of first-time revisions, dislocation rates for acetabulum-only revisions (10.28%) were significantly higher than those for both components (4.61%) and femur-only (0%) reconstructions (p = 0.025). CONCLUSION: Although patients undergoing first-time revisions had lower rates of dislocations than those undergoing repeated revisions, acetabulum-only reconstructions performed at first-time revision arthroplasty entailed an increased risk for instability.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score0.843

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 integrity0.0000.001
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 head0.025
GPT teacher head0.242
Teacher spread0.217 · 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

Citations22
Published2014
Admission routes2
Has abstractyes

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