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Record W3048308498 · doi:10.1111/jsap.13195

Placement of ureteral stents in three rabbits for the treatment of obstructive ureterolithiasis

2020· article· en· W3048308498 on OpenAlexaff
Hélène Rembeaux, Isabelle Langlois, Stacy Burdick, B. McCleery, Marilyn Dunn

Bibliographic record

VenueJournal of Small Animal Practice · 2020
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineHydroureterHydronephrosisStentNephrostomySurgeryUreterRenal functionUltrasoundComplicationUrologyUrinary systemRadiologyInternal medicinePercutaneous

Abstract

fetched live from OpenAlex

Management of ureteral obstruction with stenting is often associated with a lower rate of complications than ureterotomy in domestic carnivores, but this treatment has not been previously evaluated in rabbits. Three rabbits (7, 6 and 10 years old) were diagnosed with unilateral obstructive ureterolithiasis associated with hydronephrosis and hydroureter on abdominal ultrasound. Decreased overall renal function was confirmed in all three cases. Ureteral stents were placed retrogradely via cystotomy without complication in two cases and anterogradely via nephrostomy in the third case. Survival after stent placement was 30, 3 and 8 months, with encrustation of the stent and re-obstruction occurring 18, 1 and 6 months after stent placement in successive cases. Ureteral stenting can be considered for short-term management of ureterolithiasis in rabbits to improve renal function and maintain quality of life. Ultrasound or radiographic monitoring is recommended to detect encrustation of the stent. Studies comparing ureteral stenting to ureterotomy in rabbits are needed to determine the effectiveness of these techniques.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0020.001
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 head0.181
GPT teacher head0.358
Teacher spread0.177 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations7
Published2020
Admission routes1
Has abstractyes

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