Comparing short dental implant and standard dental implant in terms of marginal bone level changes: A systematic review and meta‐analysis of randomized controlled trials
Bibliographic record
Abstract
Abstract Purpose To compare short implants (SH; 4‐8 mm) to standard implants (ST; longer than 8 mm) in edentulous jaws, evaluating pri‐implant marginal bone levels (MBLs) changes, implant failures (IFs), complications, and prosthesis failures (PFs). Materials and Methods Electronic searches were conducted through the PubMed, Web of Science, EMBASE, Scopus, the Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov to locate all randomized controlled trials (RCTs) comparing SH to ST. Meta‐analysis procedures were performed on the weighted mean difference (WMD) and standardized mean difference (SMD) of MBLs using Stata. Results Twenty‐three articles were included in this review. The WMD of MBLs when comparing SH to ST in both jaws up to 1‐year follow‐up was statistically significant preferring SH (WMD: −0.09 [CI: −0.12, −0.06], I 2 : 67.0%). The efficacy of SH vs ST on SMD of MBLs was moderate (SMD: −0.43 [CI: −0.57, −0.28], I 2 : 55.7%). There were no significant differences in IF (RR: 0.75 [0.44,1.27]) and PF (RR: 0.58 (0.22,1.581), and significantly higher biological complications (RR: 0.25 [0.15, 0.40]) for SH was observed compared to the ST in both jaws up to 1‐year follow‐up. Conclusions SH and ST implants showed the comparable outcomes except biological complication preferring SH. Future systematic review and meta‐analysis with longer and larger RCTs are required to confirm the present outcomes.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.035 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".