Predictors of long-term stability of maxillary dental arch dimensions in patients treated with a transpalatal arch followed by fixed appliances
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it