Advances in basic science methodologies for clinical diagnosis in female stress urinary incontinence
Bibliographic record
Abstract
We provide an overview of advanced imaging techniques currently being explored to gain greater understanding of the complexity of stress urinary incontinence (SUI) through better definition of structural anatomic data. Two methods of imaging and analysis are detailed for SUI with or without prolapse: 1) open magnetic resonance imaging (MRI) with or without the use of reference lines; and 2) 3D reconstruction of the pelvis using MRI. An additional innovative method of assessment includes the use of near infrared spectroscopy (NIRS), which uses non-invasive photonics in a vaginal speculum to objectively evaluate pelvic floor muscle (PFM) function as it relates to SUI pathology. Advantages and disadvantages of these techniques are described. The recent innovation of open-configuration magnetic resonance imaging (MRO) allows images to be captured in sitting and standing positions, which better simulates states that correlate with urinary leakage and can be further enhanced with 3D reconstruction. By detecting direct changes in oxygenated muscle tissue, the NIRS vaginal speculum is able to provide insight into how the oxidative capacity of the PFM influences SUI. The small number of units able to provide patient evaluation using these techniques and their cost and relative complexity are major considerations, but if such imaging can optimize diagnosis, treatment allocation, and selection for surgery enhanced imaging techniques may prove to be a worthwhile and cost-effective strategy for assessing and treating SUI.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".