Allometry of human calvaria bones during development from birth to 8 years of age shows a nonlinear growth pattern
Bibliographic record
Abstract
Pediatric skulls change rapidly in size and shape during development, especially for children up to 8 years of age. This project was developed to address the gap in understanding of the three-dimensional growth parameters of the human skull during this period and the impact these growth patterns have on fontanelle closure and suture formation. This study offers novel data on the dynamic changes in the anatomy of the skull with the intention of providing better guidance for pediatric surgical care. Craniometric landmarks defined on three-dimensional computed tomography reconstructions were used to map skull development in children aged 0 to 8 years old. A total of 364 datasets were analyzed and statistically representative 3D skulls with anatomical craniometric features such as head shape, bone size, suture and fontanelle closure time were generated for 17 age groups spanning birth to 8 years of age to provide a comprehensive neuroanatomical understanding of how the pediatric skull changes over time. This study indicates that the cranial bones follow a non-linear growth pattern, with the occipital and frontal bones driving the directionality of fontanelle closure and delivers a 3D visualization of the developmental characteristics of the skull providing a landmark resource for understanding the growth dynamics of the human skull. While clinical measurements remain valid approaches for the planning of surgical interventions, these 3D models may provide a more accurate planning paradigm.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".