Effects of different loading protocols on the secondary stability and peri‐implant bone density of the single implants in the posterior maxilla
Bibliographic record
Abstract
BACKGROUND: Immediate or early loading of dental implants becomes a clinically feasible concept. PURPOSE: The aim was to evaluate the effects of different loading protocols (immediate, early, and delayed) on secondary stability and peri-implant bone density of single implants in the posterior maxilla. MATERIALS AND METHODS: Thirty-nine implants (Dentium, South-Korea) were placed in 39 patients. After placement, implant stability values (ISQ) and baseline peri-implant bone density values derived from cone-beam computed tomography were recorded. Thirteen implants were included randomly in each loading groups. The secondary ISQ values were recorded during follow-up visits. Peri-implant bone density values were measured 1 year after placement again. Data was statistically analyzed. RESULTS: Immediate-loaded group showed the lowest ISQ values, 1 month after placement. During the next follow-up visits, delayed-loaded group showed the lowest ISQ values while other groups showed comparable results. Early loading increased the peri-implant bone density greater than the other groups. CONCLUSIONS: The peri-implant bone of early-loaded implants was significantly denser than that of immediate- and delayed-loaded implants, 1 year after placement. Density increment can be judged as the radiological findings of loaded-bone, which may also reduce the need for histomorphometric analysis of human biopsy to evaluate the bone reaction around the implants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".