Assessment of 3‐dimensional bone augmentation of severely atrophied maxillary alveolar ridges using patient‐specific poly ether‐ether ketone (PEEK) sheets
Bibliographic record
Abstract
BACKGROUND: This study aimed to analyze the effectiveness of virtually designed polyether-ether ketone (PEEK) sheets to delineate and maintains the three-dimensional patient's maxillary alveolar ridge. MATERIALS AND METHODS: Fourteen patients (34 implants) with severely atrophied anterior maxillary alveolar ridges underwent rehabilitation using custom-made CAD/CAM PEEK sheets acting as a containment system for interpositional mix of particulate autogenous and xenogeneic bone graft, fixed by mono-cortical screws. Radiographic Assessment included measurements of linear changes in the vertical and horizontal dimensions on cross-sectional cuts of computed tomography (CBCT) using special software. RESULTS: Wound healing was uneventful for all the patients except one patient that showed wound break down 2 weeks postoperatively, which did not affect the outcome of the procedure. CBCT scans were interpreted to compare the quantity of both vertical and horizontal bone preoperatively and 6 months postoperatively. Statistical analyses demonstrated a significant difference between the results of both time intervals, with a mean vertical and horizontal bone gain was 3.47 mm(±1.46) and 3.42 (±1.1) with a P-value of (.0001). The customized sheets were removed 6 months postoperative with the successful placement of dental implants. CONCLUSION: The virtual planning of three-dimensional maxillary alveolar ridge augmentation utilizing patient-specific PEEK sheets deemed successful to restore the deficient ridge and to accommodate suitable size dental implants.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".