Geometric and volumetric relationship between human lumbar vertebrae and “Black‐bone” MRI‐based models
Bibliographic record
Abstract
BACKGROUND: This study will examine the differences between human lumbar vertebrae, three-dimensional (3D) scans of these bones, 3D models based on 'Black-bone' magnetic resonance imaging (MRI) scans, and 3D-printed models. MATERIALS AND METHODS: 3D mesh models were created from the "Black-bone" MRI data from two cadaveric human spines, and then 3D printed. Four models were analysed and compared: anatomic bones, 3D-scanned models, MRI models and 3D-printed models. RESULTS: There was no significant difference between when comparing the average of all measurements between all model types (p = 0.81). The mean dice coefficient was 0.91 (SD 0.016) and the mean Hausdorff distance was 0.37 mm (SD 0.04 mm) when comparing the MRI model to the 3D-scanned model. The mean volumes for the MRI model and the 3D scanned model were 10.42 and 10.04 ml (p = 0.085), respectively. CONCLUSIONS: The 'Black-bone' MRI could be a valid radiation-free alternative to computed tomography for the 3D printing of lumbar spinal biomodels.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".