3-D Volumetric Evaluation of Human Mandibular Growth
Bibliographic record
Abstract
Bone growth is a complex process that is controlled by a multitude of mechanisms that are not fully understood.Most of the current methods employed to measure the growth of bones focus on either studying cadaveric bones from different individuals of different ages, or successive two-dimensional (2D) radiographs. Both techniques have their known limitations. The purpose of this study was to explore a technique for quantifying the three dimensional (3D) growth of an adolescent human mandible over the period of one year utilizing cone beam computed tomography (CBCT) scans taken for regular orthodontic records. Three -dimensional virtual models were created from the CBCT data using mainstream medical imaging software. A comparison between computer-generated surface meshes of successive 3-D virtual models illustrates the magnitude of relative mandible growth. The results of this work are in agreement with previously reported data from human cadaveric studies and implantable marker studies. The presented method provides a new relatively simple basis (utilizing commercially available software) to visualize and evaluate individualized 3D (mandibular) growth in vivo.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".