Evaluation of a second-generation LED curing light.
Bibliographic record
Abstract
BACKGROUND: Light-emitting diode (LED) curing lights offer advantages over quartz-tungsten-halogen (QTH) lights, but the first-generation LED lights had some disadvantages. PURPOSE: This study compared a second-generation LED light with a QTH light to determine which was better at photopolymerizing a variety of resin composites. METHODS: The ability of a LED light used for 20 and 40 seconds to cure 10 resin composites was compared with that of a QTH light used for 40 seconds. The composites were 1.6 mm thick and were irradiated at distances of 2 and 9 mm from the light guide. The Knoop hardness at the top and the bottom of each composite was measured at 15 minutes and 24 hours after irradiation. RESULTS: The different curing lights and irradiation times did not have the same effect on all of the composites (p < 0.01). For specimens analyzed 24 hours after irradiation, the LED light used for 20 seconds cured 5 of the composites as well as when the QTH light was used for 40 seconds (p > 0.01). When used for 40 seconds, the LED light cured 6 of the composites as well as when the QTH light was used (p > 0.01), and all 10 composites achieved more than 80% of the hardness produced with the QTH light. CONCLUSIONS: This LED light could not polymerize all of the composites as well as the QTH light. However, when used for 40 seconds, it cured more than half of the composites as well as when the QTH light was used, and all of the composites achieved a hardness comparable to that produced with the QTH light.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".