Differences in shoulder belt fit for females versus males measured using upright open magnetic resonance imaging
Bibliographic record
Abstract
In comparisons of similar crashes between sexes, females exhibit an elevated risk of injury to the cervical spine and ribs. This preliminary study aims to investigate the relationship between upper body shape and shoulder belt fit, which may provide further insight into sex-based differences in seat belt loading and potential injury patterns. A non-ferromagnetic seat was fabricated for use with an open magnetic resonance (MR) imaging system, as well as a seat belt made of standard automotive webbing material with MR-visible markers. MR scans were acquired for 10 volunteers (5 female, 5 male) in an upright self-selected seat back position. This analysis focused on the shoulder belt positioning relative to the sternum and clavicle, with consideration of soft tissue interactions on this routing. Females in this study exhibited over three times greater range in the distance of the shoulder belt to the top of the sternum (SBD) compared to the males, despite similar or less variability than males in all gross anthropometric measures (SBD range, females: 21-116 mm, males: 51-78 mm). Such differences in variability highlight the diversity in routing patterns that may be influenced by different body geometries, such as breast tissue volume and distribution. Understanding how shoulder belt fit varies among and within diverse occupant populations highlights the need for improving the robustness of restraint design and performance.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".