The effect of implant‐abutment junction position on crestal bone loss: A systematic review and meta‐analysis
Bibliographic record
Abstract
PURPOSE: To investigate the effect of the apico-coronal implant position on early and late crestal bone loss (CBL), in bone and tissue level implants. MATERIALS AND METHODS: Electronic and manual literature searches were conducted for controlled clinical trials reporting on CBL before and after functional loading of implants. Random effects meta-analyses were applied to analyze the weighted mean difference (WMD) and meta-regression was conducted to investigate any potential influences of select confounding factors. RESULTS: Fourteen articles were included in the systematic review and 12 were included in the quantitative synthesis. For bone level implants, WMD comparing early CBL in equi and subcrestal placement was 0.15 mm (P = .18). For analyses of late CBL in bone level implants, equi and subcrestal placement revealed a 0.03 mm WMD (P = .88). Where in supra and subcrestal placement, WMD was 0.04 mm (P = .86). The comparison presented considerable heterogeneity between these two arms, where the P value for chi-square test presented as .006. Finally, for CBL between supra and equicrestal placement, WMD was -0.64 mm (P < .0001), favoring the supracrestal group. For tissue level implants, WM of early and late CBL in implants placed equi-crestally was 0.68 ± 0.12 mm and 0.69 ± 0.54 mm, respectively, where for implants placed sub-crestally, the WM of CBL was 1.72 ± 0.15 mm and 2.26 ± 0.63 mm, respectively. CONCLUSION: Within the limitations of this study, it is recommended to place tissue level implants equicrestally, and bone level implants subcrestally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.006 | 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".