Survival rate of dental implant placed using various maxillary sinus floor elevation techniques: A systematic review and meta-analysis
Bibliographic record
Abstract
Aim: The aim of this systematic review is to evaluate the survival rate of dental implant placed using different maxillary sinus floor elevation techniques. Setting and Design: PRISMA guidelines were used for this systematic review and meta-analysis. Materials and Methods: Relevant articles were searched from Medline, PubMed, Google Scholar, ScienceDirect, and Cochrane trials. Articles published in English language were selected. Hand search was further conducted. For risk of bias, two tools were used, i.e., Cochrane tool for randomized controlled trials (RCTs) and new castle Ottawa quality assessment tool for non-RCTs. Statistical Analysis: For statistical meta-analysis RevMan 5.4 software was used. Results: Seventeen studies were finalized. All studies were included in the meta-analysis to check the implant survival rate. There is no statistical difference between direct and indirect techniques, and forest plot was derived for direct approach (P = 0.688, 95% confidence interval [CI] 0.9691) and for indirect approach (P = 0.686 and 95% CI 0.970). Conclusion: There is no statistically significant difference in the survival rate of implant placed using direct or indirect sinus lift approach procedures. Hence, the technique is selected as per the indications given for each direct and indirect procedure.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.032 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".