Five‐Year Evaluation of Lifecore Restore® Implants: A Retrospective Comparison with Nobel Biocare MK II® Implants
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to compare survival rates and marginal bone resorption of the Lifecore (LC) Restore Implant System with the benchmark Nobel Biocare (NB) MK II Implant System. MATERIALS AND METHODS: All implants were inserted by the same surgeon and all radiological analyses were performed by the same radiologist. Two hundred ninety LC implants were analyzed radiologically after 1 year and compared with the same number of NB implants serving as a historical reference group. After 5 years, 200 LC implants could be compared with 224 NB implants. Each implant was monitored for exposed threads, as compared with the baseline registrations. RESULTS: No significant differences were found between the two implant systems regarding survival rates (LC 100% and NB 99.2%). Considering the findings of this study, the two implant systems compared might be regarded as clones. Nevertheless, because of dissimilar onset of threads, about 1 mm more implant-retaining bone anchorage is gained with the Lifecore Restore Implants as compared with NB MK II Implants. CONCLUSIONS: Based on the assumption that >3 exposed NB threads correspond to >4 exposed LC threads, significantly more bone loss (p < .01) could be demonstrated for the NB implants after 5 years. Thus, it may be justified to consider the differences in implant design to have a decisive clinical relevance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".