Clinical outcome of 302 zygomatic implants in 110 patients with a <scp>follow‐up</scp> between 6 months and 7 years
Bibliographic record
Abstract
BACKGROUND: Zygomatic implant surgery is considered as a safe and successful alternative to the conventional implant surgery with bone grafts for patients with severe atrophic maxilla. PURPOSE: The aim of this retrospective clinical case series was to report clinical outcome of zygomatic implants with a follow-up between 6 months and 7 years. MATERIALS AND METHODS: A total of 110 patients with 302 zygomatic implants were included in this study. The intra and postoperative complications and survival rate of zygomatic implants were evaluated. RESULTS: The study included 110 consecutively treated patients with an age range of 21 to 76 years (mean 57.35 years, SD 10.42). The overall zygomatic implant survival rate was 98.34%. There were five implant failures in four patients. One intraoperative and 17 postoperative complications developed in 18 patients. There were no dropouts and the median follow-up of the patients was 41.75 months (with a range of 6-89 months). CONCLUSIONS: According to the results, in cases of severely atrophic posterior maxilla, zygomatic implant surgery can be considered as an effective and safe alternative to conventional implants and bone grafting procedures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".