How do we make progress in phenotyping patients with LUT such as OAB and underactive detrusor, including using urine markers and microbiome data, in order to personalize therapy? ICI‐RS 2023: Part 1
Bibliographic record
Abstract
INTRODUCTION: Overactive bladder (OAB) and Underactive bladder (UAB) could be associated with metabolic syndrome, affective disorders, sex hormone deficiency, changes in urinary microbiota, functional gastrointestinal disorders, or autonomic nervous system dysfunction. OBJECTIVES: The aim of this Think Tank was to provide a guide on how to investigate OAB and/or detrusor underactivity (DU) patients to better clarify the underlying pathophysiology and possibly personalize the treatment. METHODS: A compendium of discussion based on the current evidence related to phenotyping patients with OAB or DU investigating metabolic, neurogical, psychological and gastrointestinal aspects with the aim to personalize the treatment. RESULTS AND CONCLUSIONS: The article emphasizes the critical significance of adopting a comprehensive yet tailored approach to phenotyping patients with lower urinary tract symptoms, such as OAB and UAB. The intricate interplay between the lower urinary tract and various factors, metabolic, neurological, psychological, and gastrointestinal can define unique LUT profiles, enabling personalized therapies to replace the one-size-fits-all approach.
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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".