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Record W2255247258 · doi:10.1302/0301-620x.96b4.31850

Outcome, revision rate and indication for revision following resurfacing hemiarthroplasty for osteoarthritis of the shoulder

2014· article· en· W2255247258 on OpenAlexaboutno aff
Jeppe Vejlgaard Rasmussen, Anne Dyhl-Polk, Anne Kathrine Belling Sørensen, Bo Sanderhoff Olsen, Stig Brorson

Bibliographic record

VenueThe Bone & Joint Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOsteoarthritisDanishArthroplastySurgerySignificant differenceInternal medicine

Abstract

fetched live from OpenAlex

In this study, we evaluated patient-reported outcomes, the rate of revision and the indications for revision following resurfacing hemiarthroplasty of the shoulder in patients with osteoarthritis. All patients with osteoarthritis who underwent primary resurfacing hemiarthroplasty and reported to the Danish Shoulder Arthroplasty Registry (DSR), between January 2006 and December 2010 were included. There were 772 patients (837 arthroplasties) in the study. The Western Ontario Osteoarthritis of the Shoulder (WOOS) index was used to evaluate patient-reported outcome 12 months (10 to 14) post-operatively. The rates of revision were calculated from the revisions reported to the DSR up to December 2011 and by checking deaths with the Danish National Register of Persons. A complete questionnaire was returned by 688 patients (82.2%). The mean WOOS was 67 (0 to 100). A total of 63 hemiarthroplasties (7.5%) required revision; the cumulative five-year rate of revision was 9.9%. Patients aged < 55 years had a statistically significant inferior WOOS score, which exceeded the minimal clinically important difference, compared with older patients (mean difference 14.2 (8.8; 95% CI 19.6; p < 0.001), but with no increased risk of revision. There was no significant difference in the mean WOOS or the risk of revision between designs of resurfacing hemiarthroplasty.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.037
GPT teacher head0.324
Teacher spread0.287 · 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 designOther design
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

Citations38
Published2014
Admission routes1
Has abstractyes

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