Effects of Early Moderate Loading on Implant Stability: A Retrospective Investigation of 634 Implants with Platform Switching and Morse‐Tapered Connections
Bibliographic record
Abstract
PURPOSE: This retrospective investigation aimed to evaluate the effect of early moderate loading (EML) on implant stability. MATERIALS AND METHODS: Following 6 weeks of conventional healing, 634 dental implants (Ankylos®, Dentsply Implants, Mannheim, Germany) inserted in 247 patients were uncovered. Provisional restorations were placed in infra-occlusion in partially edentulous patients and in full occlusion in edentulous patients. Patients were instructed to consume a soft/liquid diet until final restorations were delivered after approximately 6 weeks. Periotest values (PTVs) at the time of uncovering and after EML were assessed in order to calculate the change in PTV (ΔPTV). Improvement of the PTV was analyzed to account for dependencies between measurements on multiple implants of a single patient, along with other factors. RESULTS: No implant was lost during the EML. After a mean loading time of 3 years (± 1.7 years), the implant survival rate was 98.74%. The PTV of 556 implants decreased (improved) over the course of the study. The ΔPTV was statistically significant (p = .0001), and none of the factors analyzed appeared to influence it. CONCLUSIONS: The EML of implants does not impair the implants' stability, as determined by Periotest. On the contrary, early moderate loading seems to be beneficial at compromised bone qualities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".