Additively manufactured implant components for imaging validation studies
Bibliographic record
Abstract
Radiographic imaging is the current standard for evaluating postoperative joint replacements. Prior to application, such imaging methods need to be validated to determine the lower limits of performance under ideal conditions, using either a phantom or cadaver setup. Conventionally manufactured orthopedic implants for use in such studies are not always accessible and may be cost-prohibitive to purchase. We propose the use of additively manufactured implants as a cheaper, more accessible alternative for use in radiographic imaging validation studies. Bias and repeatability were compared between conventionally manufactured and additively manufactured reverse total shoulder implant sets under a standard model-based radiostereometric analysis phantom study environment. Measurements were compared using the humeral stem or glenosphere model relative to reference bone beads, and the humeral stem relative to the glenosphere model to measure implant relative displacement. Compared to the conventionally manufactured implants, the additively manufactured implants had less bias along the internal-external rotation axis (p < 0.001), but greater bias along the abduction-adduction and flexion-extension rotation axes (p = 0.005, 0.011). Additively manufactured implants had greater repeatability along the internal-external rotation axis (p < 0.001), but worse repeatability along the medial-lateral translation axis (p = 0.001) and the abduction-adduction rotation axis (p < 0.001). Differences were on the orders of 0.01 mm and 0.5°. For the purpose of validating two-dimensional-three-dimensional radiographic imaging techniques of orthopedic implants, additively manufactured implants can be used in place of conventionally manufactured implants, assuming they are fabricated to the manufacturer's specifications. Observed differences were within the errors of the measurement technique and not clinically meaningful.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".