Bibliographic record
Abstract
This symposium arose from a discussion of papers presented at a radiostereometry (RSA) meeting in New Orleans, February 4, 2003. RSA has become the gold standard method to quantitatively assess in vivo displacements or motions of orthopaedic interventions and devices. Owing to its sensitivity, early failures of operations and implants may often be predicted. Research into the modern incarnation of RSA began in 1974 by Göran Selvik in Lund, Sweden.2 During the first 15 years, it was used almost exclusively in the Scandinavian countries as a research tool; thereafter it was adopted by several European centers. Today, routine RSA usage has extended worldwide including to the USA, Canada and Australia, as illustrated by a number of contributions to this symposium from research groups based in those countries. This clearly demonstrates a worldwide increase of interest in RSA. There is a growing awareness within the orthopaedic community that new joint replacement prostheses, cements, and surgical techniques should be thoroughly evaluated before general release to the market, an approach that has been common in the pharmaceutical industry for decades. Regulatory bodies have begun to demand direct scrutiny of premarket clinical data used to support performance claims. According to the EU Reclassification Directive, the upward classification of hip, knee and shoulder joint replacements renders them subject to a more burdensome set of regulatory requirements.1 Currently, a number of RSA research groups are collaborating with the International Standardization Organization Working Group 4 to establish a standard of premarket clinical evaluation using RSA (CEN/TC 285/WG4).3 In its 30-year history, RSA has proven an active and evolving technique that continues to be comprehensively updated to maximize current advances in computer technologies and increases in computational power. Kärrholm et al in their overview article demonstrate RSA can be used in a broad spectrum of applications. As described by Kärrholm et al and Kaptein et al, RSA today is a method that uses automated analysis of digital images. For several implants, the problem of attaching markers to them has been solved by a recently developed technique called model-based RSA, which uses three-dimensional computer models to overcome the need for implant modification. The advances exemplified by the various studies in the symposium demonstrate not only the increasing power of RSA but, equally importantly, its practicality in a wider range of situations. The symposium provides an overview of the state of the art through a number of experimental studies and a wide-ranging series of clinical studies. Radiostereometric analysis is not limited to the knee or the hip, but can be used for the evaluation of virtually any implant or surgical technique where motion or displacement might predict failure. This is amply illustrated by the inclusion of two shoulder studies (Hallström et al; Rahme et al), one ankle study (Nelissen et al), and an experimental study on biodegradable spinal cages (Krijnen et al). Another use of RSA is as a highly accurate ruler for validation and refinement of finite element models, as demonstrated in the paper of Gill et al. We hope this collection of papers will provide readers with an appreciation of the historical impact of this highly accurate clinical measurement tool, as well as its future influence on the evaluation of new developments in arthroplasty, spine surgery, and fracture fixation. Edward R. Valstar, PhD Biomechanics and Imaging Group, Department of Orthopaedics Leiden University Medical Center, Leiden, The Netherlands; and Department of Biomechanical Engineering, Delft University of Technology, Delft, The Netherlands Richie (H.S.) Gill, DPhil, OOEC Nuffield Department of Orthopaedic Surgery, Botnar Research Center University of Oxford, Nuffield Orthopaedic Centre, Oxford, UK
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".