<b>Editorial:</b> Editors' introduction: Bladder outflow obstruction in women
Bibliographic record
Abstract
Dear Readers, We are delighted to present to you this special section of NUU journal dedicated to the comprehensive exploration and treatment of bladder outlet obstruction (BOO) in women. BOO, a condition often underrepresented in medical literature, has significant implications for female health and quality of life. This collection of research papers, reviews, and clinical perspectives aims to shed light on the complexities of BOO in women, highlighting its underlying causes, diagnostic challenges, and evolving management strategies. This special issue brings together the collective expertise of distinguished researchers and clinicians dedicated to understanding and managing BOO in women. It encompasses a wide range of topics, including epidemiology, pathophysiology, clinical evaluation, and various treatment modalities from the less to the most invasive ones. We have gathered original research studies, review articles, and clinical case reports, providing a multidimensional approach to the subject matter. The papers in this issue delve into the intricate aspects of BOO in women, addressing the challenges faced in accurate diagnosis and individualized treatment. We explore diagnostic techniques, such as urodynamic studies and imaging modalities, which play a pivotal role in understanding the complex dynamics of female BOO. Furthermore, the authors discuss innovative therapeutic interventions, both conservative and surgical, to improve symptoms, restore bladder function, and enhance the overall quality of life for affected women. Moreover, this issue recognizes the importance of multidisciplinary collaboration and emphasizes the need for a holistic approach to the management of BOO in women. The integration of urologists, gynecologists, physiotherapists, nurses, and other healthcare professionals is vital in providing comprehensive care tailored to individual patient needs. We highlight successful collaborative efforts and emphasize the importance of shared knowledge and expertise in optimizing treatment outcomes. As editors, we express our heartfelt appreciation to all the contributors who have shared their research and experiences in this field. Their invaluable contributions from the bedrock of this special issue and offer a wealth of knowledge to the medical community at large. We are confident that the insights presented within these pages will inspire further research, facilitate clinical decision-making, and ultimately improve the lives of women suffering from BOO. We invite you to explore the wealth of information presented in this special issue, encouraging discussions and debates that foster innovation and collaboration in the field. By addressing the challenges surrounding BOO in women head-on, we aim to promote better understanding, improved management strategies, and ultimately enhanced patient outcomes. Sincerely, Lysanne Campeau and Jacques Corcos MDs
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".