Quantifying light energy delivered to a Class I restoration.
Bibliographic record
Abstract
PURPOSE: To measure the amount of light energy that dental students actually deliver to a Class I preparation in a dental mannequin. MATERIALS AND METHODS: Approval for the study was obtained from the Dalhousie University Health Sciences Research Ethics Board. Each of 20 third-year dental students light-cured a Class I preparation in tooth 27 in a mannequin head. A photodetector located at the bottom of the cavity preparation measured how much light would be received by a restoration. Each student cured the simulated restoration for 20 seconds using a quartz-tungsten-halogen curing light (Optilux 401). The irradiance received (mW/cm2) was recorded in real time, and the energy per unit area (J/cm2) delivered to the detector by each student was calculated. The students were then given detailed instructions on how to effectively use the curing light, and the experiment was repeated. RESULTS: When the curing light was fixed directly over the tooth, the greatest amount of light energy delivered to the detector in 20 seconds was 13.9 +/- 0.4 J/cm2. Before instruction, the students delivered between 2.0 and 12.0 J/cm2 (mean +/- standard deviation [SD]: 7.9 +/- 2.7 J/cm2). After receiving detailed instructions, the same students delivered between 7.7 and 13.4 J/cm2 (mean +/- SD: 10.0 +/- 1.4 J/cm2). A paired student"s t test showed that instruction resulted in a significant improvement (p < 0.001). CONCLUSIONS: Although instruction yielded improvements, the mean energy delivered was much less (7.9 J/cm2 before instruction and 10.0 J/cm2 after instruction) than the expected 13.9 J/cm2. To maximize the energy delivered, the operator should wear eye protection, should watch what he or she is doing and should hold the light both close to and perpendicular to the restoration.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".