A <scp>three‐in‐one</scp> alveolar process reconstruction protocol for maxillary molar sites with severe residual bone height deficiency: A <scp>proof‐of‐concept</scp> pilot study
Bibliographic record
Abstract
BACKGROUND: Implant placement in maxillary molar sites with severe height deficiency often requires multiple surgeries, which was time-consuming, invasive, and subject to serious postoperative complications. PURPOSE: To introduce and assess a three-in-one technique (extraction, alveolar ridge preservation [ARP], and sinus elevation) for augmenting deficiency maxillary molar alveolar ridges. MATERIAL AND METHODS: Fourteen patients with severe posterior maxillary ridge height deficiency underwent extraction, sinus elevation via an intrasocket window and ARP using sticky bone and then covered with acellular dermal matrix (ADM). Primary closure was intentionally not obtained. Cone-beam computed tomography and periapical radiography were used to measure dimensional ridge changes over time. Bone biopsies were taken at implant placement 7-21 months after surgery, which proceeded without additional grafting. Peri-implant soft tissue was assessed after 8-12 months of functional loading. RESULTS: Maxillary molar sites (13 first molars, 1 second molar) with a mean sinus floor height of 1.73 ± 0.86 mm and mean buccal plate thickness of 1.62 ± 1.15 mm were elevated and grafted. Immediately after surgery, the mean sinus floor height was 14.03 ± 1.97 mm and the alveolar thickness at virtual implant platform level was 12.99 ± 1.88 mm. After 5-9 months healing, those measurements decreased by 2.45 ± 1.73 mm (p = 0.000) and 3.88 ± 3.95 mm (p = 0.006), respectively. Healed ridges were composed of 18.74% ± 4.34% mean vital bone and 19.08% ± 9.10% mean residual graft. After 8-12 months of functional loading, the peri-implant tissue appeared healthy, and there was a mean marginal bone loss of 0.12 ± 0.11 mm. CONCLUSIONS: For maxillary first molar sites with severe sinus floor height deficiency, this minimally invasive three-in-one treatment allows for uncomplicated implant placement and short-term functional 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".