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Record W2009601284 · doi:10.1002/nau.10147

Urinary bladder hyperreflexia: A rat animal model

2003· article· en· W2009601284 on OpenAlexaff
Hassan Shaker, Mohammed Sherif Mourad, Mohammed Hatem Elbialy, Mostafa Elhilali

Bibliographic record

VenueNeurourology and Urodynamics · 2003
Typearticle
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsMcGill UniversityRoyal Victoria Hospital
Fundersnot available
KeywordsHyperreflexiaMedicineAnimal modelSpinal cord injurySpinal cordUrinary systemSurgeryAnesthesiaAnatomyInternal medicine

Abstract

fetched live from OpenAlex

In this work, we are presenting a rat animal model for bladder hyperreflexia after suprasacral spinal cord transection. Our aim was to standardize an animal model that can be useful in studying this condition. After standardizing the animal model in a pilot study, 26 female Sprague-Dawley rats were subjected to spinal cord transection at the level of T10 vertebra. Four animals were subjected to cystometrogram (CMG) 24 hr after spinalization and six rats 3 weeks post-spinalization. These CMGs were compared to that of six normal controls. The detailed description of the model presented in this manuscript, is the final result after several modifications. All the animals consistently developed hyperreflexia after an initial period of spinal shock phase. Expressed volume of urine continued to decrease until it reached a plateau after peaking at 1-week post-spinalization. The attrition rate reached 27.3% after several improvements in the animal model and was mostly from self-inflicted injuries. Post-operative complications included hypothermia, decubitus ulcers, hematuria, urinary tract infection in addition to the unexplained death of two animals. In conclusion, we believe that this animal model closely resembles the clinical condition of hyperreflexia and follows similar course. The relative low cost of this animal model and the easy maintenance makes it a valuable tool to study such a condition.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.651
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.025
GPT teacher head0.302
Teacher spread0.277 · 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

Citations24
Published2003
Admission routes1
Has abstractyes

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