Biologic Response of Immediately versus Delayed Loaded Implants Supporting Ill-Fitting Prostheses: An Animal Study
Bibliographic record
Abstract
BACKGROUND: Computer-assisted preoperative implant planning and transfer toward the patient allow the production of a prosthesis prior to surgery. This implies that the prosthesis can be installed immediately following implant insertion. An inherent disadvantage of this is a cumulated error, which can lead to prosthesis misfit owing to topographic deviations of the planned versus the installed implants. PURPOSE: The aim of this study was to determine whether prosthesis misfit is compromising the osseointegration of immediately versus delayed loaded implants and whether freshly installed implants adapt to the prosthesis. MATERIALS AND METHODS: In each of five New Zealand White rabbits, two experimental conditions were compared. One tibia harbored the so-called test implant, which originally showed a vertical misfit of about 500 microm with the prosthesis to which it was tightened immediately after implant installation. The control implant was installed in the other tibia and was allowed to heal during 9 weeks before the prosthesis with the vertical misfit of about 500 microm was connected to it. The prostheses were left in place for 12 weeks, after which the animals were sacrificed. RESULTS: All implants healed uneventfully. There were no statistically significant differences between the biologic responses of test and control implants. With a three-dimensional laser scanner, significantly more displacement of the test implants toward the prostheses was observed compared with the control implants. This led to a significant decrease in prosthesis misfit for the test implants compared with the control implants. CONCLUSIONS: This study indicates that prosthesis misfit does not per se lead to biologic failure of immediately loaded or of already osseointegrated implants. In addition, immediately loaded implants seem to topographically adapt to the prosthesis, thereby minimizing the existing misfit.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".