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Record W3033497556 · doi:10.1002/nau.24415

Best practices for cystometric evaluation of lower urinary tract function in muriform rodents

2020· article· en· W3033497556 on OpenAlexafffund
Matthew O. Fraser, Phillip P. Smith, Maryrose P. Sullivan, Dale E. Bjorling, Lysanne Campeau, Karl‐Erik Andersson, Mitsuharu Yoshiyama

Bibliographic record

VenueNeurourology and Urodynamics · 2020
Typearticle
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsMcGill University
FundersNational Institute on AgingNational Institutes of HealthNational Institute of Diabetes and Digestive and Kidney DiseasesCanadian Urological AssociationU.S. Department of Veterans Affairs
KeywordsUrinary systemMedicineFunction (biology)UrologyInternal medicineBiologyCell biology

Abstract

fetched live from OpenAlex

AIMS: Rodent cystometry has provided valuable insights into the impact of the disease, injury, and aging on the cellular and molecular pathways, neurologic processes, and biomechanics of lower urinary tract function. The purpose of this white paper is to highlight the benefits and shortcomings of different experimental methods and strategies and to provide guidance on the proper interpretation of results. METHODS: Literature search, selection of articles, and conclusions based on discussions among a panel of workers in the field. RESULTS: A range of cystometric tests and techniques used to explore biological phenomena relevant to the lower urinary tract are described, the advantages and disadvantages of various experimental conditions are discussed, and guidance on the practical aspects of experimental execution and proper interpretation of results are provided. CONCLUSIONS: Cystometric evaluation of rodents comprises an extensive collection of functional tests that can be performed under a variety of experimental conditions. Decisions regarding which approaches to choose should be determined by the specific questions to be addressed and implementation of the test should follow standardized procedures.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.251
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.134
GPT teacher head0.398
Teacher spread0.264 · 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

Citations35
Published2020
Admission routes2
Has abstractyes

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