Nasal Floor Elevation for Implant Treatment in the Atrophic Premaxilla: A Within‐Patient Comparative Study
Bibliographic record
Abstract
BACKGROUND: There is a lack of evidence regarding success of implants placed in atrophic premaxilla using the nasal floor elevation technique. PURPOSE: This study aimed to compare implants placed in augmented bone in the anterior maxilla using the nasal floor elevation technique with implants placed in the maxillary sinus region using the sinus lift technique. MATERIALS AND METHODS: A within-patient controlled clinical trial was performed on 14 patients receiving 78 implants. The implants were assigned to one of two study groups on the basis of implant location. A total of 37 implants were placed in the nasal fossa region (NF group), and 41 implants were placed in the maxillary sinus region (MS group). Patients were followed up for 4.5 ± 2.2 years, with comparable follow-up times for implants in NF and MS groups (4.7 ± 2.1 and 4.9 ± 2.1 years, respectively; p > .05). Treatment outcomes were assessed and statistically analyzed. RESULTS: Implant success rate was 89.2% in the NF group and 95.0% in the MS group, with no statistically significant difference between them (p > .05). No nasal or sinus membrane perforation or other complications were reported within the follow-up period. Significant differences were found between the two groups in terms of residual bone height, augmented bone height, and implant diameter. CONCLUSIONS: Nasal floor elevation is an effective and safe procedure that can be used for implant placement in atrophic premaxilla with success rates that are comparable to those of implants placed in the maxillary sinus.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".