Primary implant stability based on alternative site preparation techniques: A systematic review and meta‐analysis
Bibliographic record
Abstract
AIM: To evaluate the effect of special implant site preparation methods in improving primary implant stability in low-density bone. MATERIAL AND METHODS: This meta-analysis included studies published in English and Mandarin Chinese up to March 31, 2022 from MEDLINE/PubMed, Embase, Scopus, and Wanfang databases. The primary stability of five site preparation methods were measured using implant stability quotient. The random-effects model was chosen for data analysis. Grading of recommendations assessment, development, and evaluation assessment was adopted as a collective grading of the overall body of evidence. RESULTS: 12 of the 17 studies included in the meta-analysis were randomized control trials. Three studies investigated osseodensification drilling (OD), eight studies examined osteotome technique (OT), five studies explored piezosurgery (PS), and four studies were conducted on under-drilling (UD). Meta-analysis showed a statistically significant increase in primary stability for the OD (mean difference [MD], 10.25; 95% CI: 4.97-15.52; p < 0.001), OT (MD, 6.34; 95% CI: 2.26-10.42; p = 0.002), and UD (MD, 11.43; 95% CI: 5.17-17.68; p < 0.001) groups when compared to the conventional drilling group, while the PS group did not (MD, 1.50; 95% CI: -2.54-5.54; p = 0.47). CONCLUSION: Significantly higher primary implant stability was shown in the OD, UD, and OT groups compared to the conventional drilling group. PS displayed the least favorable primary stability and when compared to conventional drilling, was not statistically significant.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.043 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".