The Diagnosis and Treatment of Acetabular Bone Loss in Revision Hip Arthroplasty: An International Consensus Symposium
Bibliographic record
Abstract
Despite growing evidence supporting the evaluation, classification, and treatment of acetabular bone loss in revision hip replacement, advancements have not been systematically incorporated into a single document, and therefore, a comprehensive review of the treatment of severe acetabular bone loss is needed. The Stavros Niarchos Foundation Complex Joint Reconstruction Center at Hospital for Special Surgery held an Acetabular Bone Loss Symposium on June 21, 2019, to answer the following questions: What are the trends, emerging technologies, and areas of future research related to the evaluation and management of acetabular bone loss in revision hip replacement? What constitutes the optimal workup and management strategies for acetabular bone loss? The 36 international experts convened were divided into groups, each assigned to discuss 1 of 4 topics: (1) preoperative planning and postoperative assessment; (2) implant selection, management of osteolysis, and management of massive bone loss; (3) the treatment challenges of pelvic discontinuity, periprosthetic joint infection, instability, and poor bone biology; and (4) the principles of reconstruction and classification of acetabular bone loss. Each group came to consensus, when possible, based on an extensive literature review. This document provides an overview of these 4 areas, the consensus each group arrived at, and directions for future research.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".