The functional loading of implants increases their stability: A retrospective clinical study
Bibliographic record
Abstract
PURPOSE: To assess the difference in the evolution of implant stability values, determined by resonance frequency analysis (RFA), between two groups of implants subjected to two different loading protocols: immediate and delayed. MATERIALS AND METHODS: A retrospective clinical study was conducted, including a total of 93 implants placed in 38 patients. All implants corresponded to one of two models of the Klockner Implant System (Essential Cone and Vega) and were divided into two groups according to the loading protocol adopted: delayed loading in group A (>10 weeks) and immediate loading in group B (<48 hours). Implant stability was measured four times throughout the study period with a Penguin RFA device: implant placement (T0), definitive loading (T1), 6 months after loading (T2), and 12 months after loading (T3). RESULTS: Implant stability quotient (ISQ) values showed a statistically significant increase in both groups after loading. In group A, the greatest increase in stability occurred between T1 and T2, whereas in group B, the greatest increase occurred between T0 and T1, coinciding in both cases with the period in which the implants were subjected to prosthetic loading. CONCLUSIONS: The functional loading of implants increases their stability, as measured in ISQ values by RFA. Increases in ISQ values are greater during the months immediately following loading, which shows that immediate or early loading protocols are not only possible but can also be beneficial.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".