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Record W4312195884 · doi:10.1097/spv.0000000000001274

Evidence-Informed, Interdisciplinary, Multidimensional Action Plan to Advance Overactive Bladder Research and Treatment Initiatives: Directives From State-of-the-Science Conference on Overactive Bladder and Cognitive Impairment

2022· article· en· W4312195884 on OpenAlexaff
Tatiana V. D. Sanses, Jacqueline Zillioux, Rachel High, Katherine L. Dengler, Mary F. Ackenbom, Catherine E. DuBeau, Marianna Alperin, Adrian Wagg, Lori A. Birder, Chantale Dumoulin, Tamara Bavendam

Bibliographic record

VenueUrogynecology · 2022
Typearticle
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsUniversité de MontréalUniversity of Alberta
Fundersnot available
KeywordsOveractive bladderMedicineChampionTranslational scienceUrinary incontinenceCognitionPsychologyNursingPolitical scienceAlternative medicineUrologyPsychiatry

Abstract

fetched live from OpenAlex

ABSTRACT: This article outlines an evidence-informed, interdisciplinary, multidimensional, comprehensive action plan for the American Urogynecologic Society to improve care of women with overactive bladder (OAB) while minimizing treatment-related adverse events, including cognitive impairment. It is a "call to action" to advance basic, translational, and clinical research and summarizes initiatives developed at the State-of-the-Science Conference on OAB and Cognitive Impairment to (1) develop framework for a new OAB treatment approach in women, (2) define research gaps and future research priorities, (3) champion health equity and diversity considerations in OAB treatment, (4) foster community and promote education to remove stigma surrounding OAB and urinary incontinence, and (5) elevate visibility and impact of OAB, by creating partnerships through education and engagement with health care professionals, industry, private and public payers, funding agencies, and policymakers.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score0.850

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.093
GPT teacher head0.414
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2022
Admission routes1
Has abstractyes

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