Use of skeletal maturation based on hand-wrist radiographic analysis as a predictor of facial growth: a systematic review.
Bibliographic record
Abstract
The purpose of this systematic review was to evaluate the predictive value of hand-wrist radiographic assessment of skeletal maturity in estimating facial growth timing and velocity. A search of PubMed, Medline, Cochrane Database of Systematic Reviews, Embase, Web of Sciences, and Lilacs identified 16 articles that met the following inclusion criteria: use of hand-wrist radiographs for skeletal maturation determination, facial growth evaluated through cephalometric radiographs, and cross-sectional or longitudinal studies. Five articles were rejected because of major methodological issues. Most of the remaining articles had small sample size, and there was no report of randomization or method error. Skeletal maturity determined by hand-wrist radiographic analysis was well related to overall facial growth velocity. Maxillary and mandibular growth velocities were related to skeletal maturity, but their relationship was less robust than that for overall facial growth. The available articles have not adequately defined a relationship between cranial base growth velocity and skeletal maturity. Hand-wrist radiographic assessment of skeletal maturity for use in facial growth prediction should include bone staging as well as ossification events. The role of skeletal maturity assessment in clinical and research applications is discussed and recommendations are provided.
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".