Repair or replacement of defective direct resin-based composite restorations: contemporary teaching in U.S. and Canadian dental schools
Bibliographic record
Abstract
Background. Opportunities exist to promote minimally invasive dentistry by repairing rather than replacing defective and failing direct resin-based composite restorations. The authors conducted a study to investigate the current teaching of such techniques in U.S. and Canadian dental schools. Methods. In late 2010, the authors, with the assistance of the Consortium of Operative Dentistry Educators, invited 67 U.S. and Canadian dental schools to participate in an Internet-based survey. Results. The response rate was 72 percent. Eighty-eight percent of the dental schools taught repair of defective direct resin-based composite restorations. Of these schools, 79 percent reported providing both didactic and clinical teaching. Conclusions. Although teaching repair of defective resin-based composite restorations was included in the didactic curricula of most schools, students in some schools did not gain experience in minimally invasive management of defective resin-based composite restorations by means of performing repair procedures. The American Dental Association's Code on Dental Procedures and Nomenclature does not have a procedure code for resin-based composite restoration repairs, which may limit patients' access to this dental treatment. Clinical Implications. Teaching dental students minimally invasive dentistry procedures, including restoration repair, extends the longevity of dental restorations and reduces detrimental effects on teeth induced by invasive procedures, thereby serving the interests of patients.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| 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".