Comparison of micro-computed tomography and laser scanning for reverse engineering orthopaedic component geometries
Bibliographic record
Abstract
A significant amount of research has been undertaken to evaluate the function of implanted joint replacement components. Many of these studies require the acquisition of an accurate three-dimensional geometric model of the various implant components, using methods such as micro-computed tomography or laser scanning. The purpose of this study was to compare micro-computed tomography and laser scanning for obtaining component geometries. Five never-implanted polyethylene tibial inserts of one type were scanned with both micro-computed tomography and laser scanning to determine the repeatability of each method and measured for any deviations between the geometries acquired from the different scans. Overall, good agreement was found between the micro-computed tomography and laser scans, to within 71 microm on average. Micro-computed tomography was found to have superior repeatability to laser scanning (mean of 1 microm for micro-computed tomography versus 19 microm for laser scans). Micro-computed tomography may be preferred for visualizing small surface features, whereas laser scanning may be preferred for acquiring the geometry of metal objects to avoid computed tomography artifacts. In conclusion, the choice of micro-computed tomography versus laser scanning for acquiring orthopaedic component geometries will likely involve considerations of user preference, the specific application the scan will be used for, and the availability of each system.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".