Trends in Teaching Posterior Restorations in North American Dental Schools: A Comparative Study.
Bibliographic record
Abstract
OBJECTIVES: To compare trends in teaching and placement of composite resin versus amalgam in posterior restorations in Canadian dental schools with those in the United States. METHODS: Secondary descriptive and statistical analyses were performed on data from 2 previous studies. The data consisted of responses to questionnaires on teaching policies and the proportion of posterior restorations (amalgam and composite resin) performed in Canadian and US dental schools. Fisher's exact test and 2-sample z-test were used to compare the proportions. RESULTS: Canadian dental schools allocated less time than US schools to teaching composite resin restorations (p = 0.006): 22.2% of Canadian schools versus 76.4% of US schools devoted more than 50% of preclinical teaching time to such restorations. Canadian dental schools also dedicated more time to teaching amalgam restorations (p = 0.041): 33.3% of Canadian schools versus 8.8% of US schools devoted 50-75% of preclinical teaching time to amalgam restorations. Between 2008 and 2018, a significantly higher proportion of composite resin restorations were performed in US dental schools than in Canadian schools (p < 0.001). CONCLUSIONS: In Canadian dental schools, teaching of posterior composite resin restorations was more conservative than in US schools. There was no consensus among Canadian and US dental schools on composite resin preparation techniques or contraindications. Clear, standardized guidelines pertaining to composite resin teaching policies are suggested.
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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 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".