Radiographic assessment of uncemented total hip arthroplasty: reliability of the Engh Grading Scale
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".