Volumetric computerized tomography as a measurement of periprosthetic acetabular osteolysis and its correlation with wear.
Bibliographic record
Abstract
Osteolysis, which is considered to be a major source of morbidity following total hip joint replacement, has been notoriously difficult to measure accurately, particularly in the acetabular area. In order to study periacetabular osteolysis, specialized software for computerized tomography (CT) scan image analysis has been developed. This software (3D-CT) eliminates metal artifacts, allows three-dimensional segmentation of the CT image, and reconstructs the segmented image to provide an accurate representation and measurement of volume for osteolytic lesions. In the present study, 20 patients underwent periacetabular osteolytic volume determination using 3D-CT, functional assessment (using the Harris Hip Scale, the Western Ontario and McMaster University Osteoarthritis Index, and the short form 36 questionnaire), and two-dimensional analysis of volumetic polyethylene wear using digitalized plain films. Periacetabular osteolysis correlated directly with the polyethylene wear rate (relative risk [RR] = 0.494, P = 0.027). If one patient with an acetabular revision, one patient with recurrent dislocation, and one patient with a Biomet prosthesis are excluded, then the correlation between wear and osteolysis is improved (RR = 0.685, P = 0.002). In summary, the current study demonstrates both the feasibility of CT imaging of periacetabular osteolysis and the correlation between polyethylene wear and osteolytic volume, providing a potential outcome measure for clinical trials that are designed to examine interventions in this complex disease process.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".