Rapid palatal expander vs. quad-helix in the orthodontic treatment of cleft lip and palate patients.
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to compare the results obtained using the rapid palate expander (RPE) and the quad-helix (QH) in the treatment of cleft lip and palate (CLP) patients in mixed dentition. METHODS: In this retrospective clinical trial 28 CLP patients in mixed dentition were enrolled and divided into a group RPE, consisting of 11 patients, and a group QH, consisting of 17 patients. Plaster models before and after treatment were scanned and measurements were made using the software Ortho Analyzer. Measurements, obtained with a digital gauge on plaster models, were regarded as the gold standard and compared with those on digital models. Measurement error was assessed using the Dahlberg formula and the reliability and accuracy of the measurements were evaluated by the interclass coefficient (ICC). The unpaired t-test was used to compare the group RPE with the group QH. RESULTS: Measurement error found with Dahlberg's method was 0.36. ICC values indicated a strong repeatability of the evaluator's measurements on digital models and an almost perfect repeatability on plaster models, with a value of 0.79 and 0.93, respectively. The ICC for the accuracy of the measurements on digital models was equal to 0.74, indicating a strong agreement with measurements realized on plaster models. Patients in the QH group presented a gain of the anterior palatal diameter significantly greater than those in the RPE group. CONCLUSIONS: The use in mixed dentition of the quad-helix allows obtaining a greater gain of the anterior palatal diameter and a greater symmetry of the dental arch.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".