Bibliographic record
Abstract
In addition to the series of lectures summarized in the articleson the preceding pages, the 2011 Canadian UrologyForum also included a series of workshops intended tohelp answer the question: “Does overactive bladder (OAB)exist as a separate entity or is it a symptom complex thatmay be associated with other conditions?”In an effort to arrive at an answer to this question, theparticipants were divided into groups and asked to examinea particular subset of patients with OAB. The subsets were:females, males, neurogenic and elderly. In each of theseworkshops, the participants were asked to answer the followingquestions:• What is the most likely underlying cause?• Are there unique phenotypes that can be identifiedclinically that are linked to a common underlyingpathophysiology?• What is the likelihood of associated disorders of theoutlet or pelvic floor?• What are the most effective treatments?Each of the groups then reported their answers back tothe entire group of participants, after which time they wereasked to vote on the key question. This review provides asummary of the discussions that took place in each groupand concludes with the results of the vote.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".