Composite versus Amalgam Restorations Placed in Canadian Dental Schools
Bibliographic record
Abstract
OBJECTIVES: To investigate the latest teaching policies of posterior composite placement versus amalgam and to determine the actual numbers of posterior composites versus amalgam restorations placed in Canadian dental schools, over the years from 2008 to 2018. METHODS: Emails were sent to Chairs/Heads of Restorative Departments and Clinic Directors of all 10 Canadian dental schools to collect data in the forms of: 1) Questionnaire on current teaching policies of posterior composite and amalgam restorations; 2) data entry form to collect the actual numbers of posterior composite and amalgam restorations placed in their clinics. RESULTS: For the teaching questionnaire, the response rate was 90% (n=9). Seven (78%) of the responding schools reported that they assign 25%-50% of their preclinical restorative teaching time towards posterior composite placement. While, three (33%) of the responding schools allocated 50%-75% of their restorative teaching towards amalgam placement. Data entry response rate was 80% (n=8). Amalgam material was dominant in the restoration distribution from 2008 to 2012. While from 2013 to 2018, resin composite material was dominant in all eight responding schools. Linear regression analysis revealed a significant increasing trend in placing posterior composites in all the responding schools over time (p<0.05). CONCLUSIONS: Data analysis revealed a clear trend towards an increase of posterior composite restoration placement and a decrease in the number of amalgam restorations placed. However, the teaching time assigned for posterior composite is not aligned with quantity placed. Review and adjustment of time allocated for teaching and training of each material are recommended.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".