The Impact of In Vitro <i>Accelerated Aging, Approximating 30 and 60 Years In Vivo</i>, on Commercially Available Zirconia Dental Implants
Bibliographic record
Abstract
BACKGROUND: Despite increased popularity of Zirconia dental implants, concerns have been raised regarding low temperature degradation (LTD) and its effect on micro-structural integrity. PURPOSE: This study evaluated the effect of LTD on four types of Zirconia dental implants at 0, 30, and 60 years of artificial aging. The impact of aging on t-m transformation and micro crack formation was measured. MATERIALS AND METHODS: (D). Focused ion beam-scanning electron microscopic analysis determined the micro structural features, phase transformation, and the formation of micro cracks. RESULTS: At 15 hours, type A implant presented with micro cracks and t-m transformation of 0.9 µm and 3.1 µm, respectively. At 30 hours, micro cracks remained shallow (1 µm). At 15 hours, type B implant presented micro cracks (0.7 µm) and grain transformation (1.2 µm). At 30 hours, these features remained superficial at 0.6 and 1.5 µm, respectively. Type C implant presented surface micro cracks of 0.3 µm at 15 hours. The depth of t-m transformation slightly increased to 1.4 µm. At 30 hours, number of micro cracks increased at the surface to an average depth of 1.5 µm. Depth of t-m transformation increased to an average of 2.5 µm. At 15 hours, micro cracks remained superficial (0.8 µm) for type D implant and depth of t-m transformation increased to 2.3 µm. At 30 hours, the depth of micro cracks increased to an average of 1.3 µm followed by increased t-m transformation to a depth of 4.1 µm. CONCLUSION: Depth of grain transformation remained within 1-4 µm from the surface. The effect of aging was minimal for all Zirconia 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".