Rigid‐bar loading on pregnant uterus and development of pregnant abdominal response corridor based on finite element biomechanical model
Bibliographic record
Abstract
During pregnancy, traumas can threaten maternal and fetal health. Various trauma effects on a pregnant uterus are little investigated. In the present study, a finite element model of a uterus along with a fetus, placenta, amniotic fluid, and two most effective ligament sets is developed. This model allows numerical evaluation of various loading on a pregnant uterus. The model geometry is developed based on CT-scan data and validated using anthropometric data. Applying Ogden hyper-elastic theory, material properties of uterine wall and placenta are developed. After simulating the "rigid-bar" abdominal loading, the impact force and abdominal penetration are investigated. Findings are compared with the experimental abdominal response corridor, previously developed for a nonpregnant abdomen. "Response corridor" denotes a bounded envelope in response space, within which the system responses usually lie. Results show that at low abdominal penetrations (less than 45 mm), the pregnant abdomen response is highly compatible with the nonpregnant case. While, at large penetrations, the pregnant abdomen demonstrates stiffer behavior. The reason must be the existence of a fetus in the model. This reveals that the existing response corridors would not be reliable to be extended for a pregnant abdomen. Hence, response corridor development for a pregnant abdomen is a crucial task. In this study, a new fixed-back rigid-bar loading response corridor is proposed for a pregnant abdomen using the load-penetration behavior of the developed model. This model and response corridor can help to study the pregnant uterus response to environmental loading and investigate the injury risk to the uterus and fetus.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".