Resin Composite Versus Amalgam Restorations Placed in United States Dental Schools
Bibliographic record
Abstract
OBJECTIVES: To investigate the latest teaching policies for posterior resin composite placement versus amalgam and to determine the actual numbers of posterior resin composites versus amalgam restorations placed in American dental schools from 2008 to 2018. METHODS: Emails were sent to the deans of all 66 dental schools in the United States to collect data in the forms of: 1) Questionnaire on current teaching policies of posterior composite and amalgam restorations; and 2) Data entry form to collect the actual numbers of posterior composite and amalgam restorations placed in their clinics. Descriptive statistics were used to summarize ratios of posterior restorations. Inferential analysis (chi-square test and z-test) was employed to compare posterior restoration proportions over time and within each year. Level of significance was set at 0.05. RESULTS: For the teaching questionnaire, the response rate was 52% (n=34). Seventy-six per cent of the responding schools reported that they assign 50% or more of their preclinical restorative teaching time towards posterior resin composite placement, while 50% of the responding schools devoted 25% or less towards amalgam teaching. Data entry response rate was 26% (n=17). In 2008, amalgam and resin composite restorations were placed almost equally. However, resin composite restorations were placed significantly more frequently from 2009 onwards in all responding schools. The results revealed a significant ongoing increasing trend in placing posterior resin composites in all responding schools over time (p<0.05). CONCLUSIONS: Data analysis revealed a clear trend towards an increase in posterior resin composite restoration placement and a decrease in the number of amalgam restorations. However, the time assigned for posterior resin composite teaching is not aligned with quantity of restorations placed. Review and adjustment of the time allocated for teaching and training of each material 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".