The correlation between pain perception among patients with six different orthodontic archwires and the degree of dental crowding
Bibliographic record
Abstract
INTRODUCTION: Forces generated in orthodontic treatment with fixed appliances create tension and compression zones in the periodontal ligament resulting in a painful experience for patients. In the first phase of orthodontic treatment, when leveling of teeth is needed, nickel-titanium (NiTi) archwires can be completely engaged in brackets, even in the cases of extreme crowding, exerting small forces. There is a great individual variation in the pain perception related to the application of orthodontic forces. OBJECTIVE: The aim of this study was to investigate the pain perception among patients with dental crowding after insertion of six different NiTi orthodontic archwires as a part of fixed appliances in the first stage of orthodontic treatment. METHODS: The study was conducted on a sample of 189 orthodontic patients receiving one of six different either superelastic or heat activated NiTi archwires, in the first phase of orthodontic treatment. Pain perception was evaluated in groups of patients with different degree of crowding. The modified McGill Pain Questionnaire with Visual Analogue Scale was used to evaluate the quality and intensity of pain. Statistical analysis was performed using simple descriptive statistics, and Pearson's chi-square test with statistical significance of p<0.05. RESULTS: Majority of patients reported pain as discomfort or pressure of moderate intensity caused by chewing or biting, started within 12 hours, carried on for 3-4 days, and decreased over time without self-medication. CONCLUSION: No correlation was found between pain perception among patients with different types of NiTi archwires and the degree of crowdinq.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".