Validation of Microfocus Computed Tomography in the Evaluation of Bone Implant Specimens
Bibliographic record
Abstract
BACKGROUND: Microfocus computed tomography (muCT) is an emerging technique owing to its speed, full three-dimensional information, and nondestructive properties. PURPOSE: The aim of this study was to explore the efficacy of a muCT system (Philips HOMX 161, Philips Medical Systems GmbH, Hamburg, Germany) for visualization of the bone structure around screw-type titanium implants by comparing muCT images with their histologic homologues. MATERIALS AND METHODS: Eight screw-type titanium implants were placed in the femoral condyles of two goats. After the excised implant-bone specimens were embedded in resin, three-dimensional muCT of the excised implant and bone specimens was performed. Histologic sections were subsequently made. A total of 150 histologic sections were matched with muCT images. RESULTS: Bone trabeculae were clearly visible on the muCT scans. However, bone close to the implant or present in the apical surface features of the implant could not be detected. The overall matching between muCT scans (slices) and the histologic sections was 89%. CONCLUSION: Investigation of trabecular bone around titanium implants by muCT can be considered highly reliable for determining trabecular bone parameters, with the exception of measuring direct bone-to-implant contact.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".