An up to 17‐year follow‐up retrospective analysis of a minimally invasive, flapless approach: 18 945 implants in 7783 patients
Bibliographic record
Abstract
BACKGROUND: This study investigates gender, age, jaw, implant position, loading protocol (immediate vs delayed), smoking, and type of surgery (punch vs flap) as influential factors of implant survival in a large patient collective. PURPOSE: To evaluate the survival rates of implants in patients using a mucoperiosteal punch for flapless implantation in the majority of cases in order to evaluate its medical efficacy and safety. MATERIALS AND METHODS: Between 1994 and 2015 all patients with complete data treated at the Wienerberg Dental Clinic, Vienna, Austria, were included and statistically analyzed in Cox proportional hazard (PH) models. As patients with multiple implants were included, a clustering term was added to the Cox PH model to respect pooled failures in patients. RESULTS: Of the initial 24 282 ANKYLOS/Dentsply implants placed in 8137 patients a total of 7783 patients with 18 945 implants were finally included. The mean follow-up was 2.8 ± 3.2 up to 17.9 years. Cumulative survival rates (CSRs) after 1, 3, 5, and 10 years were 98.5%, 97.7%, 96.7%, and 93.0%, respectively. Of these, 17 517 (92.5%) implants were placed minimally invasive via a flapless approach by use of the ATP-Punch with comparable survival rates as observed for flap surgery. The Cox PH models proved smoking (hazard ratio [HR] = 2.2) and implant position as significant factors of implant survival. In the maxilla, canines and third molars were identified as low risk sites in comparison to the most frequently implanted first premolar site. In the mandible, the central incisor and second premolar were identified as high-risk sites, the canine as low risk site in comparison to the most frequently placed first molar site. CONCLUSION: The analyzed data concludes the safety and medical efficacy of the ATP-Punch. The CSRs using this flapless technique are comparable to the classic surgical flap approach.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".