Effect of instruction on dental students’ ability to light-cure a simulated restoration.
Bibliographic record
Abstract
OBJECTIVE: To measure the light energy that dental students delivered to a simulated Class I restoration before training, immediately after training and 4 months after training. METHODS: Thirty-eight (38) dental students used a single light-emitting diode curing light (SmartLite iQ2, Dentsply) to cure, for 10 seconds, a simulated Class I restoration positioned in the Managing Accurate Resin Curing - Patient Simulator (BlueLight analytics inc.). The students then attended an instructional lecture and received individualized instruction on optimizing their light-curing technique. The students were retested immediately after instruction and again 4 months later (without further instruction). The irradiance and energy delivered during light-curing were calculated for each student at all 3 time points. Mean values were calculated and compared. RESULTS: Before instruction, the students delivered between 0.1 and 7.2 J/cm2 of energy (mean ± standard deviation [SD] 4.1 ± 1.7 J/cm2). After instruction, the same students delivered between 5.8 and 7.5 J/cm2 of energy (mean ± SD 6.7 ± 0.4 J/cm2). Analysis of variance and Fisher's Protected Least Significant Difference tests showed that instruction with the patient simulator led to a significant improvement in the amount of energy delivered and that the students retained this information. When retested 4 months later, the students delivered between 4.2 and 7.9 J/cm2 of energy (mean ± SD 6.1 ±1.1 J/cm2). Although this was less energy than immediately after instruction, the decline was not significant (p = 0.44). CONCLUSIONS: Provision of immediate feedback on light-curing technique and instruction on how to avoid mistakes led to a significant and lasting improvement in the amount of energy delivered by the students.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".