The effect of uterine fibroid region and depth on endometrial stress and strain: a finite element approach
Bibliographic record
Abstract
Uterine fibroids are common benign gynecological tumors that are observed in up to 80% of premenopausal women. It is understood that as the fibroid size increases, the surrounding tissues will be subject to greater loads. However, the effect of fibroid region on the uterine structure is not as clear. To better understand the mechanical loading of the endometrium due to the presence of a uterine fibroid, we developed a finite element model of the uterus to examine the effect of both fibroid depth and region in relation to the endometrium. The finite element model of the uterus, endometrium, and a uterine fibroid were created from a 3D segmentation of a patient's magnetic resonance images. This model was then loaded into ANSYS Mechanical 2023 R1, and then deformation, stress, and strain of the endometrium was measured for 24 fibroid positions (8 regions × 3 depths). The highest endometrial loads (deformation, stress, strain) were observed when the fibroid region was superior to the uterus and the depth was deep. Superior regions generated 10-20% higher loads on the endometrium in comparison to other regions, while deep locations had 5-10% higher endometrium loads when compared to superficial depths across almost all regions. A simple uterus model was used to show the effect of fibroid position on loads acting on the endometrium. This can provide insight into mechanisms of abnormal uterine bleeding and infertility and better inform clinical decision making.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".