Comparing occlusal contact quality after aligner and fixed appliance treatment using computerized occlusal analysis during 6 months of retention
Bibliographic record
Abstract
Abstract Objective Less than ideal contacts have been reported following aligner therapy, which is believed will resolve with settling, despite settling improving occlusal balance has not been scientifically confirmed. The aim of this study was to compare the outcome quality of occlusal contacts in patients treated with fixed appliances or clear aligners. Methods 39 orthodontic patients (14 treated with aligners; 25 with fixed appliances) were evaluated with a digital occlusal analysis system (T-scan10 ™), assessing Maximum Intercuspation contact simultaneity, symmetry, and relative force distribution. The Occlusion Time, the Right/Left force percentage (%R/L), the Anterior/Posterior contact ratio (RAP), and the anteroposterior Center of Force (COF) locations were recorded at treatment completion, and 3 and 6 months after. Results No significant differences in measured occlusal contact quality parameter were found between groups at treatment completion or follow-up (OT, %R, RAP nor COF position). The COF moved posteriorly and remained stable after 3 months, near to the first molar, but was located more anterior in females (p= 0.01). 10 patients finished treatment with marked asymmetry, (%R/L > 50±10%), especially in the fixed appliance group (9/25 =3 6%) versus the aligner group (1/14 = 7%). 1/3 of all patients (both groups combined) after 6 months retention had %R/L imbalances > 50±10%. Conclusions Occlusal contacts were comparable at completion of treatment with aligners or brackets and after 3-6 months of retention. Contacts increased in the posterior region with time, but settling did not improve marked asymmetry in all patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".