Angle of Convergence of Posterior Crown Preparations Made by Predoctoral Dental Students
Bibliographic record
Abstract
The aim of this study was to determine angle of convergence (AC) of posterior crown preparations made by predoctoral dental students at the University of Toronto. Ninety-one dies of students' crown preparations were digitally scanned with an in-Eos-Blue scanner (Sirona). Created images were virtually sliced at three similar locations of mesiodistal and buccolingual planes. Virtual protractor was used to determine AC of each section. Means and SDs were calculated, and data were statistically analyzed with ANOVA and student's t-test for operator's gender, experience, and tooth type. There were no significant differences among the groups except for AC of preparations grouped by tooth type (p<0.0001). The greatest mean mesiodistal AC was 26.4° found with mandibular molars, while the smallest was 16° found with maxillary premolars. ANOVA revealed significant difference in mean mesiodistal AC among groups (p<0.01). Also, greatest mean buccolingual AC was 25° found with mandibular molars, while the smallest was 20.8° found with maxillary premolars. ANOVA did not reveal significant difference in mean buccolingual AC among groups (p>0.05). Overall mean AC values were greater than ideal range of 2-5°; however, they were within ranges published for dentists/prosthodontists. Gender and experience had no significant effect on AC, but tooth type significantly affected AC.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 0.001 |
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".