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Record W2073315709 · doi:10.2106/jbjs.j.01848

A Survey on the Prevalence of Pseudotumors with Metal-on-Metal Hip Resurfacing in Canadian Academic Centers

2011· article· en· W2073315709 on OpenAlexaffabout
Paul E. Beaulé

Bibliographic record

VenueJournal of Bone and Joint Surgery · 2011
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHip resurfacingMedicineArthroplastyHip arthroplastySurgeryRetrospective cohort study

Abstract

fetched live from OpenAlex

BACKGROUND: Metal-on-metal bearings for hip arthroplasty have been in clinical use for over twenty years with excellent clinical results reported worldwide. A small percentage (<1%) of patients have developed an inflammatory response, and a more severe inflammatory response termed pseudotumor has been more recently reported. The primary purpose of this study was to determine the prevalence of pseudotumor following metal-on-metal hip resurfacing in Canadian academic centers. METHODS: Nine of the fourteen centers that perform metal-on-metal hip resurfacings were surveyed. The number of metal-on-metal hip resurfacing arthroplasties performed at each center was determined, as was the number of patients who presented with a pseudotumor and subsequently required revision hip surgery between 2002 and December 2008. RESULTS: At the nine centers, 3432 hip resurfacing arthroplasties were performed; 76.9% of the patients were male, the mean age was 51.2 years (range, sixteen to eighty-three years), and the mean body-mass index was 28.1 (range, 17 to 55). The mean length of follow-up was 3.4 years (range, two to nine years). A pseudotumor developed after four of the 3432 arthroplasties, for a prevalence of 0.10%. CONCLUSIONS: Although pseudotumors remain a concern after metal-on-metal hip resurfacing, the prevalence at short to midterm follow-up is very low in this multicenter survey. Continued close monitoring is required to determine what clinical factors are involved with the uncommon pseudotumor formation.

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)0.003
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 score0.006
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.065
GPT teacher head0.249
Teacher spread0.185 · 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

Citations69
Published2011
Admission routes2
Has abstractyes

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