Bibliographic record
Abstract
A discussion of the clinical aspects of overactive bladder (OAB) comprises this section of outstanding review articles.The definition of the condition, OAB, has evolved over time.Urgency in particular, the most bothersome symptom, is relatively easy to define but difficult to measure.Dr. Alan Wein analyzes the topic, beginning with the initial definition of OAB and moving through its updates, and analyzes the potential problems in the application of this definition in clinical practice.Dr. Victor Nitti reviews the clinical testing of OAB.Despite its completely clinical definition, some tests may help to diagnose the condition.Their indications and techniques are well analyzed by Dr. Nitti.Treatment of primary OAB varies slightly in male and female populations.Dr. Linda Cardozo goes through the particularity of diagnosis and treatment in females, while Dr. Christopher Chapple focuses on these aspects in males.Finally, considerations of two special populations complete this important review: neurogenic bladder overactivity is deeply analyzed by Dr. Clare Fowler, while Dr. Adrian Wagg describes the best approach in elderly patients.Any physician dealing in practice with OAB should attentively read this practical section, which provides a most accurate update on this difficult condition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".