Improving Light‐Curing Instruction in Dental School
Bibliographic record
Abstract
Delivering an inadequate amount of light to a light-cured resin will result in a resin that is inadequately cured. This study measured the radiant exposure that students delivered to a simulated restoration to determine if instruction with immediate feedback increased the amount of light they delivered. The amount of light (radiant exposure in J/cm(2)) delivered to a simulated restoration by sixty-three dental students using the same curing light for twenty seconds was recorded. The experiment was repeated after the students had been given detailed light-curing instructions together with immediate feedback using the MARCPS system. Initially, the students delivered between 1.4 and 17.5 J/cm(2) (mean±SD: 9.8±3.5 J/cm(2)). After receiving instructions and feedback on their light-curing technique, they delivered between 6.7 J/cm(2) and 17.8 J/cm(2) (mean±SD: 13.2±3.3 J/cm(2)). ANOVA and Fisher's post hoc multiple comparison tests showed that providing immediate feedback on the students' light-curing technique made a significant improvement in the radiant exposure they delivered (p<0.05). It was concluded that many dental students in this study were not using the curing light properly. After the students had received one session of additional instruction and immediate feedback using the MARC-PS, they delivered 35 percent more light energy to the same simulated restoration. Students who were closer to graduation showed a greater improvement in their light-curing technique (p=0.0091).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".