Immediate Occlusal Loading of Brånemark Implants Applied in Various Jawbone Regions: A Prospective, 1‐Year Clinical Study
Bibliographic record
Abstract
BACKGROUND: The original protocol for dental implant treatment ad modum Brånemark was based on submerged healing prior to loading. For patients, immediate implant function could reduce cost and increase attractiveness of implant treatment. PURPOSE: The goal of this study was to evaluate the short-term success rate of immediately loaded implants placed in various regions of the jaws. MATERIALS AND METHODS: Forty-one patients received a total of 127 immediately loaded implants (76 maxillary and 51 mandibular). Seventy-one percent of the patients received their prosthetic restoration the same day and the others within 11 days. All prosthetic constructions were in full contact in centric occlusion. Clinical follow-up examinations were performed at 1 week, 2 weeks, and at 1, 2, 3, 6, and 12 months after implant loading. The study was completed 1 year after loading. RESULTS: Twenty-two implants were lost in 13 patients (including 7 maxillary implants lost in 1 patient). The cumulative success rate of the implants was 82.7% after 1 year of prosthetic loading. All sites with implant losses were re-implanted, using a two-stage technique, with no further complications reported. Ninety-one percent of implants placed in regions other than the posterior maxilla were successful compared with 66% of implants placed in the posterior maxilla. Implants in patients with a parafunctional habit (bruxers) were lost more frequently than those placed in patients with no parafunction (41% vs. 12%). Implants subjected to guided bone regeneration were more successful compared with those not subjected to regeneration procedures (90% vs. 67%). CONCLUSIONS: The immediate loading concept is a realistic treatment alternative in various jawbone regions except for the posterior part of the maxilla. High occlusal loads should be considered a risk factor. On the other hand, implants in combination with bone defects frequently are penetrating cortical layers to a higher extent, thereby contributing to implant stability during the healing phase and consequently do not inevitably jeopardize the treatment result. However, further controlled clinical studies with larger sample sizes need to be performed to evaluate the influence of different parameters on treatment outcome.
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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 | 0.003 |
| 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 | 0.001 | 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".