Low-dose CT-based implant motion analysis is a precise tool for early migration measurements of hip cups: a clinical study of 24 patients
Bibliographic record
Abstract
Background and purpose - Early implant migration is known to be a predictive factor of clinical loosening in total hip arthroplasty (THA). Radiostereometric analysis (RSA) is the gold standard used to measure early migration in patients. However, RSA requires costly, specialized imaging equipment and the image process is complex. We determined the precision of an alternative, commercially available, CT method in 3 ongoing clinical THA studies, comprising 3 different cups.Materials and methods - 24 CT double examinations of 24 hip cups were selected consecutively from 3 ongoing prospective studies: 2 primary THA (1 cemented and 1 uncemented) and 1 THA (cemented) revision study. Precision of the CT-based implant motion analysis (CTMA) system was calculated separately for each study, using both the surface anatomy of the pelvis and metal beads placed in the pelvis.Results - For the CTMA analysis using the surface anatomy of the pelvis, the precision ranged between 0.07 and 0.31 mm in translation and 0.20° and 0.39° for rotation, respectively. For the CTMA analysis using beads the precision ranged between 0.08 and 0.20 mm in translation and between 0.20° and 0.43° for rotations. The radiation dose ranged between 0.2 and 2.3 mSv.Interpretation - CTMA achieved a clinically relevant and consistent precision between the 3 different hip cups studied. The use of different hip cup types, different CT scanners, or registration method (beads or surface anatomy) had no discernible effect on precision. Therefore, CTMA without the use of bone markers could potentially be an alternative to RSA to measure early migration.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".