Simulation of brain-skull development utilizing a hybrid model
Bibliographic record
Abstract
This paper describes a hybrid model which includes both standard finite element and rigid bodies for a clinical application involving skull-brain co-development in infants, with particular application for craniosynostosis modeling. To accommodate the rapid expanding brain during the first few months after birth, the skull needs to extend fast enough to increase its inner volume. Sutures that are fibrous tissues uniting the skull plates together are the major sites for skull growth during this period. At the sutures, the skull develops along these fronts in order to try to maintain the unossfied state itself. Craniosynostosis, which is a developmental abnormality, occurs when one or more sutures are fused early in life (even in utero) while the skull is growing, resulting in an abnormal skull shape. Surgery is required to reopen the suture and reduce the excessive intracranial pressure, but leaving neurosurgeons difficulties without any predicting model to assist surgical plan. Before achieve our final goal (predict patient-specific post-surgical head development), we aim to firstly study normal brain-skull growth by computer simulation, which requires a head model and appropriate mathematical algorithms for brain and skull growth respectively. On the basis of previous model, we further specified suture model into fibrous and cartilaginous sutures and develop mathematical model for skull extension. We were able to produce a series of cranial shape indices along the simulation, part of which discrepancies from reference data due to instability of the model. Some potential future work would be discussed to maintain the stability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".