Radiostereometric analysis using clinical radiographic views: Validation with model-based radiostereometric analysis for the knee
Bibliographic record
Abstract
Radiostereometric analysis is a sophisticated radiographic technique with high measurement accuracy. In order to improve the accessibility of radiostereometric analysis for clinical use, a modified radiostereometric analysis procedure has been previously proposed that enables clinical radiographic views to be used for radiostereometric analysis. It has been successfully validated for its application to the hip wear study with the conventional bead-based radiostereometric analysis environment using computed radiography. In this study, we describe the implementation and validation of this technique for the knee study with the model-based radiostereometric analysis environment using digital radiography. A knee-joint phantom with 6 degrees of freedom was examined, and the bias and repeatability/reproducibility of the modified radiostereometric analysis approach were investigated following the newly updated ASTM recommendations. The bias parameters (mean ± 95% confidence interval) ranged from 0.008 ± 0.003 mm to 0.027 ± 0.006 mm for translation and from 0.014° ± 0.007° to 0.040° ± 0.020° for rotation. The repeatability standard deviation ranged from 0.004 to 0.020 mm for translation and from 0.005° to 0.015° for rotation. The 95% repeatability limit ranged from 0.011 to 0.055 mm for translation and from 0.014° to 0.041° for rotation. The reproducibility standard deviation ranged from 0.004 to 0.023 mm for translation and from 0.006° to 0.040° for rotation. The 95% reproducibility limit ranged from 0.012 to 0.063 mm for translation and from 0.016° to 0.112° for rotation. The modified procedure allows routine clinical radiographs to be used for radiostereometric analysis, which provides the possibility of adding quantitative measurements to current patient registries.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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".