Classifying Failed Hip Arthroplasty: Generalizability of Reliability and Validity
Bibliographic record
Abstract
Interrater reliability and validity of a radiographic severity classification was evaluated in 81 patients having revision hip arthroplasty. Severity was rated separately on the femoral and acetabular sides using a five-level scale ranging from no significant loss of bone stock to uncontained loss of bone stock and discontinuity. Three academic orthopaedic surgeons rated preoperative anteroposterior radiographs taken within 6 weeks of surgery. Interrater reliability was 0.54 (weighted kappa) with 57% agreement on the acetabular side and 0.56 with 52% agreement on the femoral side. Rater to intraoperative findings agreed 45% of the time and weighted kappa was 0.41 on the acetabular side and agreed 38% of the time with weighted kappa of 0.39 on the femoral side. When radiographic and intraoperative ratings disagreed, 30% of the time no bony defect was found on the acetabular side. Fifty-eight percent of femoral radiographic ratings were upgraded intraoperatively. These results differ from previously reported results of high reliability from one institution with trained raters. A reliable and valid severity classification that is generalizable to multiple raters from different institutions is required to stratify patients for intervention studies, and to aid preoperative planning. Training in the classification system may improve generalizability.
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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 | 0.073 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".