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Record W3114934565 · doi:10.1089/cren.2020.0141

Long-Term Passive Ureteral Dilatation with Double-J Stent: Possibly an Effective Treatment for Recurrent Renal Colic Caused by Papillary Renal Necrosis

2020· article· en· W3114934565 on OpenAlexaff
Braulio O. Manzo, Eduardo Tejeda, Ben H. Chew, Pompeyo Alarcon, Edson Flores, João Torres

Bibliographic record

VenueJournal of Endourology Case Reports · 2020
Typearticle
Languageen
FieldMedicine
TopicKidney Stones and Urolithiasis Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineRenal papillary necrosisRenal colicObstructive uropathySurgeryKidneyNecrosisKidney stonesStentUrinary systemInternal medicinePathology

Abstract

fetched live from OpenAlex

Background: An uncommon cause of recurrent renal colic is mucous tissue passage secondary to renal papillae necrosis. Because of its low prevalence, the correct management of recurrent obstructive uropathy produced by renal papillary necrosis (RPN) is not well defined. Case Presentation: We present a case of recurrent renal colic associated with the expulsion of mucous tissue in a young woman's urine with a history of excessive consumption of nonsteroidal anti-inflammatory drugs (NSAIDs). The patient required multiple admissions to the emergency department because of recurrent episodes of renal colic. A retrograde pyelogram and histopathologic study of the expulsed tissue supported the diagnosis of RPN. The patient was managed with Double-J stents for 12 months, complete withdrawal of NSAIDs, and large volume intake of water. A satisfactory outcome was seen radiologically and endoscopically after treatment. The patient stopped experiencing new renal colic episodes because of the passive ureteral dilatation despite still presenting the mucous tissue expulsion in the urine. Conclusions: Passive ureteral dilatation with Double-J stents could possibly be an effective treatment for patients with recurrent renal colic secondary to persistent renal papillae necrosis.

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.026
GPT teacher head0.308
Teacher spread0.282 · 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 designCase report
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

Citations1
Published2020
Admission routes1
Has abstractyes

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