Effect of Preparation Designs on the Prognosis of Porcelain Laminate Veneers: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
SUMMARY Objective: To investigate the association between preparation designs and prognosis of porcelain laminate veneers (PLVs). Methods: Electronic and manual literature searches were performed in Medline, Embase, CENTRAL, and Scopus databases for randomized controlled trials and retrospective and prospective cohort studies comparing any two of three preparation designs. The quality of the included studies was assessed using the Newcastle-Ottawa scale. Pooled hazard ratios and risk ratios were used to evaluate the difference between two preparation designs. Subgroup analyses, sensitivity analysis, and evaluation of publication bias were performed if possible. Results: Of 415 screened articles, 10 studies with moderate to high quality were included in the meta-analysis. Comparison of preparations with incisal coverage to preparations without coverage revealed a significant result based on time-to-event data (hazard ratio=1.81, 95% confidence interval [CI]=1.18-2.78, I2=12.5%), but the result was insignificant based on dichotomous data (risk ratio=1.04, 95% CI=0.59-1.83, I2=42.3%). The other comparisons between any two of overlap, butt-joint, and window types revealed no statistically significant difference. Subgroup analyses regarding the porcelain materials, location of prosthesis, and tooth vitality could account for only part of the heterogeneity. No evidence of publication bias was observed. Conclusions: Within the limitation of the present study, it can be concluded that preparation design with incisal coverage for PLVs exhibits an increased failure risk compared to those without incisal coverage. The failure risk of the overlap type may be higher than the butt-joint type but must be validated in further studies.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.039 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".