Standing open magnetic resonance imaging improves detection and staging of pelvic organ prolapse
Bibliographic record
Abstract
INTRODUCTION: The role of imaging in pelvic organ prolapse (POP) assessment is unclear. Open magnetic resonance imaging (MRI) systems have a configuration that allows for imaging women with POP in different positions. Herein, we use a 0.5 Tesla open MRI to obtain supine, seated, and standing images. We then compare these images to evaluate the impact of posture on detection and staging of POP. METHODS: Women presenting with symptoms of POP at a tertiary care university hospital were asked to participate in this prospective cohort study. Symptom scores, POP-Q staging and three-position MRI imaging of the pelvis data were collected. The pubococcygeal line (PCL) was used to quantify within-patient changes in pelvic organ position as defined by: no displacement, <1 cm inferior to the PCL, mild (1-3 cm), moderate (3.1-6 cm), and severe (>6 cm) in the axial and sagittal T2-weighted images. Statistical analysis was completed (T-test; p<0.05 significant). RESULTS: A total of 42 women, age range 40-78 years, participated. There was a significant difference in the mean values associated with anterior prolapse in the supine (0.7±1.8), seated (2.4±3.4), and upright (4.2±1.6) positions (p=0.015). There was a significant difference in the mean values associated with apical prolapse in the supine (0.5±1.5), seated (1.5±1.4), and upright (2.1±1.5) positions (p=0.036). CONCLUSIONS: Our findings suggest that POP is more readily detected and upstaged with standing MRI images as compared to supine and seated positions. The developed two-minute standing MRI protocol may enable clinicians to better assess the extent of POP.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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".