Comparison of peri‐implant clinical outcomes of digitally customized and prefabricated abutments: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Digitally customized abutments are increasingly used in contemporary implant prosthodontics. PURPOSE: This systematic review and meta-analysis aimed at comparing the peri-implant clinical outcomes of digitally customized and prefabricated abutments. MATERIALS AND METHODS: The search strategies included electronic databases (PubMed, Embase, Scopus, and Cochrane clinical trials database) and related journals up to September, 2020. A qualitative and quantitative synthesis was performed on data extracted from the included studies. RESULTS: Three RCTs (number of patients = 120; number of dental implants = 120) and two prospective cohort studies (number of patients = 144; number of dental implants = 144) with one to three-year follow-up periods were included. The quantitative analyses did not demonstrate a significant difference between digitally customized and prefabricated abutments for peri-implant pocket depth (P = 0.62), plaque index (P = 0.67), bleeding on probing (P = 0.43), keratinized mucosa width (P = 0.75), and pink aesthetic score (P = 0.30) at one-year follow-up visit. The qualitative analyses for marginal bone level change, calculus accumulation, implant survival rate, implant success rate, white aesthetic score, and patient-reported outcomes did not demonstrate a significant difference between two groups during 1 to 3-year follow-up visits. CONCLUSION: The current data do not provide evidence of significant differences between two abutment fabrication methods in terms of peri-implant clinical outcomes within short-term period (CRD42020170807).
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| 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 | 0.002 | 0.001 |
| 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".