Advancements in dental implantology: The alveolar ridge split technique for enhanced osseointegration
Bibliographic record
Abstract
The alveolar ridge split (ARS) technique is a pivotal advancement in dental implantology, addressing the limitation of insufficient bone width for implant placement. This review traces the historical development of ARS from its initial conceptualization to current practices and future directions. Emphasizing the technique's development, indications, procedural overview, and osteotomy variations, we highlight its minimally invasive nature, which reduces patient morbidity and treatment time. This article reviews various osteotomy methods within ARS, examining their applications, benefits, and limitations. Furthermore, it discusses the technique's role in expanding treatment options for patients with compromised alveolar structures, underpinned by a high implant survival rate and the potential for immediate implant placement. We also cover the necessity of meticulous surgical technique, the importance of patient-specific factors, and the promising future of ARS facilitated by advancements in biomaterials and regenerative medicine. In summary, this review provides a comprehensive overview of ARS, offering valuable insights for dental professionals and informing future clinical practices and research in implantology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".