Craniosynostosis assessment using curvature distribution modes
Bibliographic record
Abstract
The fusion of cranial skull plates in utero, or early in life, will result in an abnormal skull shape, also called craniosynostosis; from Greek origin ("σύν" and "ὀστέον"), meaning a closure of the bone sutures.This condition is generally treated with surgery, but the planning and evaluation is based on subjective criteria which depend on the experience of the craniofacial surgery team.We have developed a modelling tool to assess whether the skull shape can be recognized by a data-driven analysis similar to a bottom-up machine learning algorithm, and we use this to quantify the outcome of surgery objectively.In this study, we evaluated five scaphocephaly, six trigonocephaly, two brachycephaly and two plagiocephaly patients both preoperatively and postoperatively.Based on the kurtosis of the curvature distributions, we were able to classify the different types of craniosynostosis, and to quantitatively evaluate the postoperative results as being closer to a normal skull shape.In conclusion, we were able to design an algorithm automatically recognizing the type of craniosynostosis and quantitatively evaluating the surgical results as being closer or further away from a normal skull.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".