Effect of disposable infection control barriers on light output from dental curing lights.
Bibliographic record
Abstract
PURPOSE: To prevent contamination of the light guide on a dental curing light, barriers such as disposable plastic wrap or covers may be used. This study compared the effect of 3 disposable barriers on the spectral output and power density from a curing light. The hypothesis was that none of the barriers would have a significant clinical effect on the spectral output or the power density from the curing light. METHODS: Three disposable barriers were tested against a control (no barrier). The spectra and power from the curing light were measured with a spectrometer attached to an integrating sphere. The measurements were repeated on 10 separate occasions in a random sequence for each barrier. RESULTS: Analysis of variance (ANOVA) followed by Fisher's protected least significant difference test showed that the power density was significantly less than control (by 2.4% to 6.1%) when 2 commercially available disposable barriers were used (p < 0.05). There was no significant difference in the power density when general-purpose plastic wrap was used (p > 0.05). The effect of each of the barriers on the power output was small and probably clinically insignificant. ANOVA comparisons of mean peak wavelength values indicated that none of the barriers produced a significant shift in the spectral output relative to the control ( p > 0.05). CONCLUSIONS: Two of the 3 disposable barriers produced a significant reduction in power density from the curing light. This drop in power was small and would probably not adversely affect the curing of composite resin. None of the barriers acted as light filters.
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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 | 0.003 |
| 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) | 0.002 | 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".