Maxillary sinus floor elevation using hydroxyapatite nano particles vs tenting technique with simultaneous implant placement: A randomized clinical trial
Bibliographic record
Abstract
PURPOSE: The present study was conducted to evaluate the amount of bone height gain, density values, and implant stability after maxillary sinus floor elevation using graftless tenting technique or the use of Hydroxyapatite Nanoparticles bone substitute for augmentation with simultaneous implant placement. MATERIALS AND METHODS: A total of 20 sinuses with a residual alveolar bone height ranging from 4-6 mm were divided into two groups and underwent sinus augmentation using nano hydroxyapatite bone substitute material and the graftless tenting technique with simultaneous implant placement. Computed tomography CT scans and ISQ measurements were conducted to evaluate bone quality, quantity, and implant stability. RESULTS: Radiographic analysis revealed that the mean bone height gain of the nano group was (7.0 ± 0.8 mm) compared to (5.0 ± 1.5 mm) in the tent group, which was statistically significant (P = .002) being higher in the nano group. The mean bone density value of the nano group was (548 ± 25 HU) compared to (420 ± 23 HU) in the tent group, which was statistically significant (P < .001) being higher in the nano group. The mean ISQ value after 6 months in the nano group was (78 ± 5) compared to (77 ± 5) in the tent group, which was statistically nonsignificant (P = .901). CONCLUSION: Nano hydroxyapatite bone graft offered superior results in terms of the bone height gain and the relative bone density as compared to graftless tenting technique. However, both techniques showed accepted results regarding implant stability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".