Impact of Platform Switching on Peri‐Implant Bone Remodeling around Short Implants in the Posterior Region, 1‐Year Results from a Split‐Mouth Clinical Trial
Bibliographic record
Abstract
AIM: To assess the effect of platform switching on peri-implant bone remodeling around short implants (8.5 mm) placed in the resorbed posterior mandibular and maxillary region of partially edentulous patients. MATERIALS AND METHODS: Seventeen patients with one or more missing teeth at both sides in the posterior region were, according to a split-mouth design, randomly assigned to be treated with a platform-matched (control) implant on the one side and a platform-switched implant (test) on the other side. A total of 62 short implants (8.5 mm) with a dual-acid etched surface with nanometer-sized calcium phosphate particles was placed. Follow-up visits were conducted one month and one year after placing the implant crown. Outcome measures were interproximal bone level changes, implant survival and clinical parameters. RESULTS: One year after loading, peri-implant bone remodeling around test implants (0.53 ± 0.54 mm) was significant less than around control implants (0.85 ± 0.65 mm; p = .003). With regard to implant survival and clinical parameters no significant differences were observed between test and control implants. CONCLUSIONS: This study suggested that peri-implant bone remodeling is affected by platform switching. One year after loading, interproximal bone levels were better maintained at implants restored according to the platform switching concept.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".