Polymerization with the argon laser: curing time and shear bond strength.
Bibliographic record
Abstract
The objective of this study was to determine the efficiency of an argon laser in polymerizing a light-cured orthodontic adhesive. Metal brackets were bonded to 185 premolars, divided into 5 different protocol groups of 37 each as follows: light 40-second buccal, light 40-second lingual, laser 5-second lingual, laser 10-second lingual, and laser 15-second lingual. All bonded specimens were placed in distilled water for 30 days at 37 degrees C followed by thermal cycling for 24 hours. Brackets were detached using a shearpeel load delivered by an Instron machine. The site of bond failure was examined under 10x magnification. The difference in the shear-peel bond strength between the light 40-second buccal (13.31 MPa) and the light 40-second lingual (11.95 MPa) groups was not statistically significant. The mean shear-peel bond strengths for the laser cured groups were quite similar for the 5-, 10- and 15-second laser groups (10.86, 11.32, and 10.80 MPa). The difference in mean lingual bond strength between the light 40-second and laser 5-second groups was not statistically significant (t = 1.26; P = .212). The adhesive remnant index analysis revealed principally cohesive bond failures. An increased frequency of enamel fractures at debond was noted in the lingual light-cured and 10-second laser-cured groups, at 35.1% (13/37) and 21.6% (8/ 37), respectively. All other groups displayed enamel fractures of 16.2% (6/37). A 5-second cure using an argon laser produced bond failure loads comparable to those obtained after 40 seconds of conventional light cure, with less than half the frequency of enamel fracture at debond.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".