Long-term skeletal stability after maxillary advancement with distraction osteogenesis in cleft lip and palate patients
Bibliographic record
Abstract
OBJECTIVE: To systematically review the long-term skeletal stability after maxillary advancement with distraction osteogenesis (DO) in cleft lip and palate (CLP) patients. MATERIALS AND METHODS: Electronic databases, grey literature, and reference list searches were conducted. The inclusion criteria were stability of maxillary advancement with distraction osteogenesis assessed at the posttreatment follow-up ≥ 1 year in CLP patients. Full articles were retrieved from abstracts or titles that appear to meet the inclusion criteria or lacked sufficient detail for immediate exclusion. Once full articles were collected, they were again reviewed considering more detailed inclusion criteria for a final selection decision. A methodologic quality assessment tool was utilized. RESULTS: Thirty abstracts/titles met the initial search criteria, and 13 articles were finally selected. Overall, methodologic quality scores were high in only one randomized clinical trial. After maxillary advancement with DO in CLP patients, the long-term horizontal relapse in A-point was less than 15% in eight studies and between 20% and 25% in four studies. The study that was judged as a high-quality study reported 8.2% horizontal relapse in A-point. The relapse rate was higher in DO with external distracter device than DO with internal distracter device. CONCLUSIONS: Current evidence suggests maxillary advancement with DO has good stability in CLP patients with moderate and severe maxillary hypoplasia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".