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 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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".