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Record W3188494336 · doi:10.1002/nau.24763

Rodent models of ketamine‐induced cystitis

2021· review· en· W3188494336 on OpenAlexaff
Saki Sultana, Geraint Berger, Ashley Cox, Christine Lehmann

Bibliographic record

VenueNeurourology and Urodynamics · 2021
Typereview
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsDalhousie University
Fundersnot available
KeywordsKetamineMedicinePathologicalRodent modelRodentDiseaseAnimal modelTranslational researchInterstitial cystitisBioinformaticsPharmacologyUrinary systemInternal medicinePathologyAnesthesiaBiology

Abstract

fetched live from OpenAlex

AIMS: Long-term or recreational use of ketamine affects the urinary system and can result in ketamine-induced cystitis (KIC). Rodent models of KIC are important to study KIC pathophysiology and are paramount to the future development of therapies for this painful condition. This review aims to provide a summary of rodent models of KIC, focusing on disease induction, experimental methods, and pathological features of the model. METHOD: A literature search was performed using the National Center for Biotechnology Information (NCBI) Pubmed database up to March 2021. 20 articles met the inclusion criteria and were finally selected. RESULTS: There are considerable variations in the rodent models used for studying KIC in terms of the strain of the animal being used; dose, duration, and route of ketamine administration to induce KIC, and assessment of pathological features. CONCLUSION: KIC remains difficult to fully recapitulate in humans. Improved characterization of KIC models and the experimental parameters and meticulous discussion on translational limitations are required to improve the translational value of research using rodent models of KIC.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.060
GPT teacher head0.336
Teacher spread0.276 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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