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Record W4313196230 · doi:10.1055/s-0042-1757161

Effect of Battery Discharge on the Output from Budget Light-Curing Units

2022· article· en· W4313196230 on OpenAlexaff
Afnan O. Al‐Zain, Ibrahim M. Alshehri, Hattan M.H. Jamalellail, Richard Bengt Price

Bibliographic record

VenueEuropean Journal of General Dentistry · 2022
Typearticle
Languageen
FieldMedicine
TopicDigital Imaging in Medicine
Canadian institutionsDalhousie University
Fundersnot available
KeywordsIrradianceBattery (electricity)Computer sciencePower (physics)StatisticsMedicineEnvironmental scienceMathematicsPhysicsOptics

Abstract

fetched live from OpenAlex

Abstract Objectives The manufacturers of budget light-curing units (LCUs) often claim to provide high-quality units that are equivalent to LCUs from major manufacturers. This study investigated the effects of battery discharge on the light output from different budget LCUs compared to a major manufacturer. Materials and Methods Two brands of budget LCUs (LY-A180 and LED-CL) were compared to a control LCU from a major manufacturer (3M). The LCUs were fully charged, and their light outputs were measured over one battery discharge cycle using repeated 10-second exposures at a 0-mm distance. Statistical Analysis Data were analyzed using one-way analysis of variance and Bonferroni post-hoc test. Results The budget LCUs delivered fluctuating light output values. In their first exposure, the budget LCUs delivered between 205 and 444 mW power, an irradiance between 533 and 1154 mW/cm2, and a radiant exposure between 5.3 and 11.5 J/cm2. As the number of exposures increased, their light output decreased between 24 and 81%, while the control LCU showed only a 4.9% decrease in power and irradiance. The light outputs from the budget LCUs were significantly less than the control LCU, and they were significantly from each other. Conclusion The budget LCUs tested could not maintain their power, irradiance, and radiant exposure output values as the battery discharged. This supports the recommendation that clinicians should be very cautious when using budget LCUs in their clinical practice.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.258
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2022
Admission routes1
Has abstractyes

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