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Record W6893876167 · doi:10.5281/zenodo.4443071

Shear Bond Strength of Orthodontic Brackets Bond with Conventional Light Emitting Diode and Fast Halogen Light

2018· article· en· W6893876167 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2018
Typearticle
Languageen
FieldDentistry
TopicDental materials and restorations
Canadian institutionsCollège Montmorency
Fundersnot available
KeywordsCrossheadHalogen lampBond strengthUniversal testing machineAdhesiveShear (geology)

Abstract

fetched live from OpenAlex

ABSTRACT Aim: To compare the mean shear bond strength (MSBS) of metal brackets bonded with conventional LED or fast halogen units. Methods: This in-vitro comparative study was conducted on 50 extracted human premolars which were randomly divided into 2 groups of 25 teeth each, by lottery method. Following a standard bonding protocol, Group 1, teeth brackets were cured with conventional LED light source and in group 2, by fast halogen light. MSBS was measured using universal testing machine, at a crosshead speed of 1 mm/minute and recorded in megapascals (MPa). Independent t-test was used for comparison of MSBS in both the groups. The level of significance was determined at p≤0.05. Duration of this study was January 2017 to October 2017. Results: MSBS of metal brackets cured with fast halogen (17.60±3.7 MPa) was similar to conventional LED unit (16.66±3.0 MPa). Conclusion: Fast halogens are equally effective for curing brackets, as they give similar bond strength when compared with conventional LED lights.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.021
GPT teacher head0.247
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2018
Admission routes1
Has abstractyes

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