Comparison of Stud Retentor Versus Bar-Clip Attachment as Implant Supported Systems Used in Overdentures: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
This study aimed to perform a systematic review and meta-analysis to analyze the results obtained clinically for bar-clip versus stud-retainers in overdentures. Three databases (PubMed Central, MEDLINE, and BvSalud) were used beyond a manual search. The study followed strictly the inclusion and exclusion criteria, considering the PICO strategy. For the risk of bias and quality assessment of studies, in the case of RCT, there were six domains of analysis, and for non-RCT studies, the Modified Newcastle-Ottawa Scale was performed. A meta-analysis was developed using the available data for marginal bone loss (MBL) and survival rate. 25 studies were included. The stud-retentor had the lowest implant SR (87.6%) and the greatest MBL (1.96 mm). For the bar-clip system, the mean survival rate was 95.91%, with only 4 studies included for this system, and the mean MBL was 1.13 mm. Only 3 studies directly compared both systems quantitatively, showing a significantly greater MBL toward the stud-retention group. The results may not allow determination of the best system for overdenture (stud retentor or bar-clip). Therefore, most of the studies suggested the stud-retentor as a more preferable system due to better distribution of forces, biological peri-implant behavior, low-cost, and ease for removal facilitating the sanitization and/or repair.
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 | 0.022 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.048 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".