Trends in Resin Composite versus Amalgam Restorations Placed in North American Dental Schools
Bibliographic record
Abstract
Many surveys have been conducted to determine the approach in teaching posterior composite in dental schools in North America. However, none of these surveys have correlated the numbers of posterior restorations placed by students in North American dental schools’ clinics with their teaching policies. In this thesis, three studies have been conducted to investigate the trends of posterior composites and amalgam restorations placed in North American dental schools. The first study investigated the latest teaching policies of posterior composite placement versus amalgam and determined the actual numbers of posterior composites versus amalgam restorations placed in Canadian dental schools from 2008 to 2018. Results revealed a clear trend toward an increase in posterior composite restorations placement and a decrease in the number of amalgam restorations placed. However, the teaching time assigned for the posterior composite is not aligned with the quantity placed. The second study investigated the latest teaching policies of posterior composite placement versus amalgam and determined the actual numbers of posterior composites versus amalgam restorations placed in U.S. dental schools from 2008 to 2018. Results indicated a definite trend toward an increase in the placement of posterior composite restorations and a decline in the placement of amalgam restorations, suggesting a misalignment between the amount of time assigned to teach each restorative material with the number of posterior restorations placed in U.S. schools’ clinics. The third study compared the trends of posterior resin composites and amalgam restorations placed in Canadian dental schools to their counterpart in U.S. dental schools. Results suggested that Canadian dental schools’ conduct toward posterior resin composite teaching is relatively conservative compared to U.S. dental schools. Furthermore, there was no consensus on posterior resin composite preparation techniques or contraindications among Canadian and U.S. dental schools. Based on the findings of these studies, it is suggested that the time devoted to teaching each restorative material in preclinical courses among North American dental schools should be revised and adjusted. In addition, clear, unified guidelines pertaining to resin composite teaching policies should be readily available among North American dental schools.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".