Phantom validation of a novel RSA-based impingement metric to assess component-on-component impingement risk
Bibliographic record
Abstract
Component-on-component impingement in total hip arthroplasty may lead to post-operative complications including dislocation. Despite numerous clinical studies focusing on reducing this risk, assessment methods remain limited to qualitative radiography, finite element analysis, and cadaver studies. There is a need for more precise measurements of impingement in the research setting. We aimed to validate a novel RSA-based impingement metric to measure component-on-component impingement in vivo. A phantom experiment of a standard metal-on-polyethylene total hip system was performed. RSA examinations were performed as typical for a traditional weight-bearing RSA exam for large joints. The phantom was placed in 10 possible impinged positions and one neutral position. Double exposure radiographs were taken to measure repeatability. The closest distance between the skirt of the head and the inner circumference of the acetabular cup liner was measured to assess impingement risk. Distances between the closest point of the hood to the edge of the cup in 10 impinged positions ranged from 0.05 to 1.03 mm, with the average being 0.67 mm. In the neutral position, the distance measured is 11.02 mm. Excellent repeatability was observed, with a standard deviation of 0.03 mm with an r value of 0.09. A validated RSA-based risk metric was established to evaluate in vivo hip impingement. A 1 mm threshold may be proposed to define impingement where distances approaching 1.00 mm are at a greater risk of impingement. This simplified metric holds promise for upcoming clinical studies on component-on-component impingement.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".