Evaluation of Magnesium-based Medical Devices in Preclinical Studies: Challenges and Points to Consider
Bibliographic record
Abstract
Absorbable metallic implants have been under investigation for more than a century. Animal and human studies have shown that magnesium (Mg) alloys can be safely used in bioresorbable scaffolds. Several cardiovascular and orthopedic biodegradable metallic devices have recently been approved for use in humans. Bioresorbable Mg implants present many advantages when compared to bioabsorbable polymer or nonabsorbable metallic implants, including similar strength and mechanical properties as existing implant-grade metals without the drawbacks of permanence or need for implant removal. Imaging visibility is also improved compared to polymeric devices. Additionally, with Mg-based cardiovascular stents, the risk of late stent thrombosis and need for long-term anti-platelet therapy may be reduced as the host tissue absorbs the Mg degradation products and the morphology of the vessel returns to a near-normal state. Absorbable Mg implants present challenges in the conduct of preclinical animal studies and interpretation of pathology data due to their particular degradation process associated with gas production and release of by-products. This article will review the different uses of Mg implants, the Mg alloys, the distinctive degradation features of Mg, and the challenges confronting pathologists at tissue collection, fixation, imaging, slide preparation, evaluation, and interpretation of Mg implants.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".