A prospective, multi‐center study assessing early loading with short implants in posterior regions. A 3‐year post‐loading follow‐up study
Bibliographic record
Abstract
BACKGROUND: Few prospective studies about early loading of short implant have been available and very little evidence exists on the outcomes longer than 3 years. PURPOSE: To assess clinical and radiographic outcomes of 6 mm-short implants placed in the posterior maxilla and mandible applying an early loading protocol. MATERIALS AND METHODS: Ninety-five short implants (6 mm-short, Ø 4 mm) were placed in 45 subjects at 3 study sites, 2 or 3 implants per subject, using a one-stage surgical procedure and loaded with a screw-retained splinted fixed prosthesis 6 weeks later. Follow-up took place at 6, 12, 24, and 36 months after loading. Marginal bone level changes, implant survival, clinical variables, and adverse events were assessed. RESULTS: The survival rate for all implants placed was 95.8%. From implant loading to 3 years follow-up, mean marginal bone level changes were minimal (0.07 ± 0.49 mm) and the peri-implant soft tissue status was healthy. No major technical or biological complications occurred except for the 4 early implant losses. CONCLUSION: Three-year data indicates that the use of splinted 6 mm-short implants is a viable treatment in posterior regions with low marginal bone resorption. Early loading after 6 weeks should be taken cautiously in patients with known risk factors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".