Methodology to measure seat belt fit in relation to skeletal geometry using an upright open MRI
Bibliographic record
Abstract
OBJECTIVE: Poor seat belt fit can result in submarining behavior and injuries to the lower extremity and abdomen. While previous studies have explored seat belt fit relative to skeletal landmarks using palpation, medical imaging remains the gold standard for visualizing and locating skeletal landmarks and soft tissues. The goal of this study was to create a method to image automotive postures and seat belt fit from the pelvis to the clavicle using an Upright Open MRI. METHODS: The posture and belt fit of 10 volunteers (5M, 5F) were measured in an Acura TLX in each subject's preferred driving posture and a standard reclined posture, and then reproduced in a custom non-ferromagnetic seat replica in the MR scanner with an MRI-visible seat belt. The MRI sequence and coil placement were designed to yield clear visualization of bone, soft tissue borders, and the seat belt markers in separate scans of the pelvis, lumbar, thoracolumbar, and thoracic regions. A process was developed to precisely register the scans, and methods for digitizing spinal and pelvic landmarks were established to quantify belt fit. CONCLUSIONS: This method creates opportunities to study variation in seat belt fit in different automotive postures, for occupants of different sexes, ages, BMIs, anthropometries, and for pregnant occupants.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".