Influence of a Lubricating Gel (Orthospeed®) on Pain and Oral Health-Related Quality of Life in Orthodontic Patients during Initial Therapy with Conventional and Low-Friction Brackets: A Prospective Randomized Clinical Trial
Bibliographic record
Abstract
The aim of this study was to investigate whether statistically significant differences exist regarding pain and the impact on oral quality of life of orthodontic treatment. A conventional brackets system was compared with low-friction brackets. A total of 90 patients (male = 35, female = 55) were chosen for this randomized clinical trial. Pain was assessed at 4, 8, and 24 hours and 2, 3, 4, 5, 6, and 7 days after the start of treatment using the McGill Pain Questionnaire. Oral health-related quality of life (OHRQoL) was assessed using the Oral Health Impact Profile-14 (OHIP-14) questionnaire. Oral quality of life was assessed at one month, with patients with low-friction brackets describing lower levels of pain. The patients with conventional brackets indicated a worse impact on their quality of life compared to the group with low-friction brackets. Statistically significant differences were found between the groups, with maximum pain observed between the first 24 and 48 hours, and the values of minimum pain are reached after 7 days. The pain and impact on oral quality of life was statistically worse in patients with conventional brackets compared to patients with low-friction brackets. The type of bracket system used was therefore shown to influence patients' perceptions of pain and impact on their OHRQoL.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".