Five-Year Clinical Performance of Complex Class II Resin Composite and Amalgam Restorations—A Retrospective Study
Bibliographic record
Abstract
The aim of this retrospective study was to investigate the clinical performance of posterior complex resin composite (RC) and amalgam (AM) restorations after a five-year period. One hundred and nineteen complex Class II restorations placed by dental students were evaluated using the USPHS criteria. Data were analyzed using Chi-square, Mann–Whitney, and Wilcoxon tests at a 0.05 level of significance. After five years, the percentages of clinically satisfactory complex Class II RC and AM restorations were 78% and 76.8%, respectively. The main reasons for the failure of AM restorations included secondary caries (Bravo—10.1%), defective marginal adaptation (Charlie—8.7%), and fracture of the tooth (Bravo—7.2%). RC restorations presented failures related to the fracture of the restoration (Bravo—16%) and defective marginal adaptation (Charlie—8.2%). There was a significantly higher incidence of secondary caries for AM restorations (AM—10.1%; RC—0%; p = 0.0415) and a higher number of fractures for RC restorations (AM—4.3%; RC—16%; p = 0.05). Regarding anatomy, AM restorations presented a significantly higher number of Alfa scores (49.3%) compared to RC restorations (22.4%) (p = 0.0005). The results of the current study indicate that complex class II RC and AM restorations show a similar five year clinical performance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".