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Record W4378710494 · doi:10.3390/biomedicines11061539

Assessing Neurogenic Lower Urinary Tract Dysfunction after Spinal Cord Injury: Animal Models in Preclinical Neuro-Urology Research

2023· review· en· W4378710494 on OpenAlexafffund
Adam Doelman, Femke Streijger, Steve J. A. Majerus, Margot S. Damaser, Brian K. Kwon

Bibliographic record

VenueBiomedicines · 2023
Typereview
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsInternational Collaboration On Repair DiscoveriesVancouver Spine Surgery InstituteUniversity of British Columbia
FundersVGH and UBC Hospital FoundationMitacsU.S. Department of Defense
KeywordsDetrusor sphincter dyssynergiaSpinal cord injuryDyssynergiaMedicineNeurogenic bladder dysfunctionAnimal modelUrinary systemUrinary bladderCystometryUrethral sphincterUrinary bladder diseaseUrologySpinal cordUrinary incontinenceInternal medicine

Abstract

fetched live from OpenAlex

Neurogenic bladder dysfunction is a condition that affects both bladder storage and voiding function and remains one of the leading causes of morbidity after spinal cord injury (SCI). The vast majority of individuals with severe SCI develop neurogenic lower urinary tract dysfunction (NLUTD), with symptoms ranging from neurogenic detrusor overactivity, detrusor sphincter dyssynergia, or sphincter underactivity depending on the location and extent of the spinal lesion. Animal models are critical to our fundamental understanding of lower urinary tract function and its dysfunction after SCI, in addition to providing a platform for the assessment of potential therapies. Given the need to develop and evaluate novel assessment tools, as well as therapeutic approaches in animal models of SCI prior to human translation, urodynamics assessment techniques have been implemented to measure NLUTD function in a variety of animals, including rats, mice, cats, dogs and pigs. In this narrative review, we summarize the literature on the use of animal models for cystometry testing in the assessment of SCI-related NLUTD. We also discuss the advantages and disadvantages of various animal models, and opportunities for future research.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.923
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.004
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 head0.431
GPT teacher head0.553
Teacher spread0.123 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations10
Published2023
Admission routes2
Has abstractyes

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