Reliability of three-dimensional anterior cranial base superimposition methods for assessment of overall hard tissue changes: A systematic review
Bibliographic record
Abstract
OBJECTIVE: The purpose of this systematic review was to synthesize the available literature concerning the reliability of three-dimensional superimposition methods when assessing changes in craniofacial hard tissues. MATERIALS AND METHODS: Four electronic databases were searched. Two authors independently reviewed potentially relevant articles for eligibility. Clinical trials, cohort, case-control, and cross-sectional studies that evaluated the reliability of three-dimensional superimposition methods on the anterior cranial base were included. RESULTS: Six studies fulfilled the inclusion criteria. Four studies used the voxel-based registration method, one used the landmark-based method and one used the surface-based method. Regarding reliability, the voxel-based studies showed on average a difference of 0.5 mm or less between images. The optimized analysis using a six-point correction algorithm in the landmark-based method showed 1.24 mm magnitude of error between images. CONCLUSIONS: Although reliability appears to be adequate, the small sample size and high risk of bias among studies make available evidence still insufficient to draw strong conclusions.
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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 | 0.021 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| 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".