Predictors of long-term stability of maxillary dental arch dimensions in patients treated with a transpalatal arch followed by fixed appliances
Bibliographic record
Abstract
BACKGROUND: The aim of this retrospective study was to identify which dental and/or cephalometric variables were predictors of long-term maxillary dental arch stability in patients treated with a transpalatal arch (TPA) during the mixed dentition phase followed by full fixed appliances in the permanent dentition. METHODS: Thirty-six patients, treated with TPA followed up by full fixed appliances, were divided into stable and relapse groups based on the long-term presence or not of relapse. Intercuspid, interpremolar and intermolar widths, arch length and perimeter, crowding, and upper incisor proclination were evaluated before treatment (T 0), post-TPA treatment (T 1), post-fixed appliance treatment (T 2), and a minimum of 3 years after full fixed appliances' removal (T 3). A binary logistic regression was performed thereafter to evaluate the impact of the dental arch and cephalometric measurements at T 1 and the changes between T 0 and T 1 as predictive variables for relapse at T 3. RESULTS: The proposed model explained 42.7 % of the variance in treatment stability and correctly classified 72.2 % of the sample. Of the seven predictive variables, only upper anterior crowding (p = 0.029) was statistically significant. For every millimeter of decreased crowding at T 1 (after TPA treatment/before starting the fixed orthodontic treatment), there was an increase of 3.57 times in the odds of having stability. CONCLUSIONS: The best predictor of relapse was maxillary crowding before treatment. The odds of relapse increase by 3.6 times for every millimeter of crowding at baseline.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".