Evaluation of conventional and CT‐based radiostereometric analysis for inducible displacement measurements after total hip arthroplasty
Bibliographic record
Abstract
Though radiostereometric analysis (RSA) is the gold standard for migration tracking, computed tomography-based RSA (CT-RSA) does not require marker beads and is available for clinical adoption. This study investigated CT-RSA in comparison to RSA for assessing hip implant stability with inducible displacement (ID) examinations. Patients (n = 48) from a previous study returned to be re-examined for femoral stem stability with CT-RSA and RSA imaging. Implant migration since patients last follow-up was calculated as a measure of stability. ID was assessed between alternated leg rotation scans for CT-RSA and between supine and weight-bearing scans for RSA. Measurements from ID and double examinations were compared between CT-RSA and RSA. All stems were well-fixed with migration <0.2 mm/year. ID measurements were lower with CT-RSA than RSA for distal translation (mean difference = 0.122 mm, p < 0.0001), total translation (mean difference = 0.158 mm, p < 0.0001), and total rotation (mean difference = 0.449°, p < 0.0001). The ID and double exam were significantly different for total translation and total rotation for CT-RSA, and significantly different for medial, distal, and total translation, and total rotation for RSA. Precision ranged from 0.049 to 0.130 mm in translation and 0.061° to 0.220° in rotation for CT-RSA, and from 0.108 to 0.269 mm in translation and 0.151° to 0.670° in rotation for RSA. ID measurements from both CT-RSA and RSA were minimal, consistent for a cohort with well-fixed stems. CT-RSA demonstrated superior precision in all axes compared to RSA. Clinical Significance: Future work should explore the use of CT-RSA in patients with suspected loosening as a potential diagnostic tool.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".