Convergence Angle of Preparations for Lithium Disilicate Glass‐Ceramic Crowns by Dental Students and Its Effect on Crown Retention
Bibliographic record
Abstract
The aims of this study were to determine the convergence angles of posterior teeth prepared by dental students at the University of Toronto for lithium disilicate glass-ceramic (LDGC) CAD/CAM crowns and to investigate their effect on loss of retention rate. A total of 280 preparations for posterior monolithic LDGC CAD/CAM crowns were performed on 270 patients (169 women and 101 men). Crowns were cemented with RelyX Unicem and Calibra Universal resin cements. Mesial, distal, and angle of convergence were measured on the bitewing radiographs. Cemented crowns were followed for up to six years. Data were analyzed for tooth type and location and for operator experience. The results showed the majority of convergence angles were greater than the recommended guidelines but fell within a clinically acceptable range (20 to 24 degrees). However, angles of convergence for mandibular molar preparations were highest (28.06±5.50 degrees), while maxillary premolars exhibited the lowest values (24.72±6.59 degrees). No significant difference was found between the results of dental students and foreign-trained dentists. Over a six-year observation period, only two crowns lost retention. The findings of this study indicated that ideal taper angles were impractical and difficult to achieve in clinical education settings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".