The evaluation of manual 2D/3D registration technology and its potential to deduce prosthetic wear in patients with metalon‐metal hip resurfacing prostheses
Bibliographic record
Abstract
Metal‐on‐metal hip resurfacing arthroplasty (MoMHRA) has been a popular alternative treatment for young patients with hip osteoarthritis. Despite its advantages over total hip arthroplasty, MoMHRA remains controversial. Malpositioning of the metal components can result in abnormal levels of blood metal ions in the patient; and yet, post‐operative management using 2D x‐rays contain high variance leading to poor detection of prosthetic wear. The purpose of this study is to determine whether 2D/3D registration technology can generate accurate acetabular implant measurements; and, whether 3D data can correlate to metal ion counts to deduce wear. Virtual 3D pelvic models (n=72) and acetabular implants were manually superimposed over 2D x‐ray images according to anatomical landmarks to measure acetabular inclination and version angles. CT models were generated for validation. No significant difference was reported between 2D vs. 3D vs. CT data, suggesting measurements were similar to the results of the gold standard CT model; although 3D measurements were more precise compared to 2D. Furthermore, there was no significant correlation in either 2D or 3D measurements compared to metal ion levels, although a stronger trend is demonstrated in 3D measurements. The findings of this study are inconsistent with the reports in literature and so further investigation is required. Supported by Queen's Graduate Award.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".