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Record W2169347714 · doi:10.1503/cjs.002610

Radiographic assessment of uncemented total hip arthroplasty: reliability of the Engh Grading Scale

2011· article· en· W2169347714 on OpenAlexaffvenue
Susan W. Muir

Bibliographic record

VenueCanadian Journal of Surgery · 2011
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsWestern University
Fundersnot available
KeywordsInter-rater reliabilityMedicineGrading (engineering)ArthroplastyOrthopedic surgeryRadiographyReliability (semiconductor)SpecialtyPhysical therapyIntra-rater reliabilityTotal hip arthroplastyScale (ratio)Rating scaleSurgeryStatisticsConfidence intervalPsychiatryInternal medicineMathematics

Abstract

fetched live from OpenAlex

BACKGROUND: Radiographic evaluation has a prominent place in the follow-up of long-term results of uncemented total hip arthroplasty (THA). The most prominent scale reported in studies is the Engh Grading Scale, but there is a lack of literature on the reliability of the scale. METHODS: We evaluated intra- and interrater reliability of the Engh Grading System for uncemented THA using 26 follow-up radiographs of patients who had primary uncemented THAs. Four evaluators with different skill levels and specialties participated: 2 arthroplasty surgeons, an orthopedic resident and a radiologist. Reliability was measured using a weighted κ coefficient for paired comparisons among the evaluators. RESULTS: Intrarater reliability was dependent on the skill and specialty of the evaluator, with the highest values achieved for the arthroplasty surgeons (κ = 0.52 and κ = 0.68) and the lowest values for the radiologist (κ = 0.14). Interrater reliability was comparable among participants, regardless of skill or specialty, and rated a moderate level of reliability (κ = 0.29-0.41) for all pairings. CONCLUSION: The Engh Grading Scale appears to be reliable when used by a single, experienced arthroplasty surgeon. Caution must be exercised when multiple raters are used, regardless of experience, as the interrater reliability achieved lower ratings.

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)0.000
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 score0.004
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.243
Teacher spread0.206 · 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
Published2011
Admission routes2
Has abstractyes

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