Evaluation of Eye Protection Filters Used with Broad-Spectrum and Conventional LED Curing Lights
Bibliographic record
Abstract
The high irradiance and the different emission spectra from contemporary light curing units (LCU) may cause ocular damage. This study evaluated the ability of 15 eye protection filters: 2 glasses, 1 paddle design, and 12 dedicated filters to block out harmful light from a monowave (HP-3M ESPE) and a broad-spectrum (Valo, Ultradent) LED LCU. Using the anterior sensor in the MARC-Patient Simulator (BlueLight Analytics) the irradiance that was delivered through different eye protection filters was measured three times. The LCUs delivered a similar irradiance to the top of the filter. The mean values of the light that passed through the filters as percent of the original irradiance were analyzed using two-way ANOVA followed by Tukey test (a= 0.05). The emission spectra from the LCUs and through the filters were also obtained. Two-way ANOVA showed that the interaction between protective filters and LCUs significantly influenced the amount of light transmitted (p< 0.001). Tukey test showed that the amount of light transmitted through the protective filters when using the HP-3M-ESPE was significantly greater compared to when using the Valo, irrespective of the protective filter tested. When using the HP-3M-ESPE, the Glasses filter allowed significantly more light through, followed by XL 3000, ORTUS, Google Professional, Gnatus filters. The Valo filter was the most effective at blocking out the harmful light. Some protective filters were less effective at blocking the lower wavelengths of light (<420 nm). However, even in the worst scenario, the filters were able to block at least 97% of the irradiance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".