Composite versus Amalgam Restorations Placed in Canadian Dental Schools
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 it