Radiostereometric analysis using clinical radiographic views: Validation measuring total hip replacement wear
Bibliographic record
Abstract
Radiostereometric analysis (RSA) is a stereo X-ray technique used in clinical research studies to evaluate micro-motion and wear of orthopaedic implants within bone. While highly accurate and precise, its adoption has been limited due to technical requirements such as the need for implanted marker beads and radiograph view angles determined by a calibration cage. We propose a new technique that separates the calibration procedure from the patient examination, enabling clinical radiograph views to be used for RSA measurements. The concept of a reference plate was adapted to establish the link between calibration procedure and patient examination procedure for cassette radiography, which may not be necessary for digital radiography. A hip wear phantom was used to validate this technique by comparing the error and repeatability of the novel procedure with that of conventional RSA. Femoral head penetration was measured versus the acetabular cup (head/cup) and marker beads in the acetabular liner (head/liner). Conventional RSA had lower inferior-superior average error (p = 0.03 for head/cup) while the modified RSA had lower anterior-posterior average error (p = 0.01). Average error was greater but not significantly so for the medial-lateral (p = 0.06) and 3D (p = 0.97) measurements. The head/liner method had lower average errors (p < 0.0001) for both procedures, but did not affect repeatability, which was similar between techniques. The novel procedure's average error and repeatability was therefore, similar to conventional RSA. This new technique could be applied to any joint with two clinical radiograph view angles pending further validation in subjects. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 34:1521-1528, 2016.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".