Effect of reduced exposure times on the microhardness of 10 resin composites cured by high-power LED and QTH curing lights.
Bibliographic record
Abstract
PURPOSE: To compare the effect of reduced exposure times on the microhardness of resin composites cured with a "second-generation" light-emitting diode (LED) curing light and a quartz-tungsten-halogen (QTH) curing light. METHODS: Ten composites were cured with a LED curing light for 50% of the manufacturers" recommended exposure time or a QTH light at the high power setting for 50% of the recommended time or on the medium power setting for 100% of the recommended time. The composites were packed into Class I preparations in extracted human molar teeth and cured at distances of 2 or 9 mm from the light guide. The moulds were separated, and the Knoop microhardness of the composites was measured down to 3.5 mm from the surface. RESULTS: The LED light delivered the greatest irradiance at 0 and 2 mm, whereas the QTH light on the standard (high power) setting delivered the highest irradiance at 9 mm. According to distribution-free multiple comparisons of the hardness values, at 2 mm from the light guide the LED light (50% exposure time) was ranked better than or equivalent to the QTH light on the high power setting (50% exposure time) or on the medium power setting (100% exposure time). At 9 mm, the LED light was ranked better than or equivalent to the QTH light (both settings) to a depth of 1.5 mm, beyond which composites irradiated by the LED light were softer (p < 0.01). At both distances, the QTH light operated on the high power setting for 50% of the recommended exposure time produced composites that were as hard as when they were exposed on the medium power setting for 100% of the recommended exposure time. CONCLUSIONS: The ability to reduce exposure times with high-power LED or QTH lights may improve clinical time management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".