Evaluation of clinicians’ use of fast-curing and budget light-curing units in Saudi Arabia
Bibliographic record
Abstract
BACKGROUND: Light-curing units (LCUs) are essential for polymerizing resin-based composites, and a lack of understanding will likely compromise the quality of the final restoration. The understanding and clinical application of LCU features—including the use of fast-curing modes and budget LCUs—require investigation. This study assesses the knowledge and practice patterns regarding LCU features among dental professionals. METHODS: A self-administered questionnaire was distributed among specialized dentists in restorative dentistry, other dental specialists, general practitioners, and students/interns. The survey assessed participants’ knowledge and practices regarding LCU ergonomics, curing modes, use of budget LCUs, and their potential impact on restoration outcomes. It consisted of 23 main questions, in addition to 7 demographic and 4 eligibility questions. Data were analyzed using one-way ANOVA with post-hoc Scheffé tests to compare knowledge and practice scores across groups (α = 0.05). RESULTS: A total of 338 questionnaires were completed (80.5% response rate). Specialized restorative dentists had significantly higher LCU knowledge scores (mean=64.31) compared to other specialists (48.84) and students/interns (53.27) (p<0.001). Clinical practice scores followed a similar trend (p=0.007). Notably, 33.4% of respondents were unsure if they were using a budget LCU, and 59.6% did not check the LCU irradiance of the lights used in their clinic. Only 4.2% measured the irradiance from the LCU daily. CONCLUSION: Significant gaps exist in the knowledge and practice about LCUs, particularly among non-restorative specialists and students. The widespread lack of awareness regarding budget LCUs and irradiance verification highlights a need for standardized education on LCU selection, curing modes, and routine output testing in clinical settings.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".