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Record W2888106160 · doi:10.22374/jeleu.v1i1.14

Chemolysis of a Uric Acid Stone in a Horseshoe Kidney

2018· article· en· W2888106160 on OpenAlexvenueno aff
Malathy Balakrishnan, Yih Chyn Phan, Jonathan Cobley, Wasim Mahmalji

Bibliographic record

VenueJournal of Endoluminal Endourology · 2018
Typearticle
Languageen
FieldMedicine
TopicKidney Stones and Urolithiasis Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHorseshoe kidneyAbdomenHorseshoe (symbol)PelvisUrinary systemRadiologySurgeryKidneyAnatomyInternal medicine

Abstract

fetched live from OpenAlex

Chemolysis of kidney stone is not unheard of. However, to our knowledge, there is no previous report of chemolysis of a kidney stone in a horseshoe kidney. We report the first ever case of chemolysis of a stone in a horseshoe kidney. As part of his visible haematuria workup 4 years ago, a 66-year-old gentleman with a history of gout was found to have a horseshoe kidney. In early 2017, he was seen in the urology clinic with some non-specific abdominal pain without a recent history of visible haematuria, lower urinary tract symptoms, and urinary tract infections. His CT KUB (computed tomography of kidneys, ureters and bladder), revealed a 1.3cm stone in his horseshoe kidney [Figure 1 and 2]. At the same time, his CT KUB has also picked up some retroperitoneal lymphadenopathy in the abdomen and pelvis which were suspicious of lymphoma. His serum uric acid level was noted to be normal. Subsequently, he underwent a laparoscopic right iliac lymph node biopsy which confirmed nodal marginal zone non-Hodgkin's B-cell lymphoma. He was reviewed by the haematology team and they decided to adopt a watch and wait approach to his disease with quarterly CT CAP (computed tomography of chest, abdomen and pelvis) scans. During this period of time, he had several gout attacks and he was started on allopurinol i.e. 100mg once a day. He also considerably increased his daily fluid intake. 6 months after his initial CT KUB, he was found to be completely stone free on his CT scan [Figure 3 and 4].

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.606

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.014
GPT teacher head0.295
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.

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

Citations1
Published2018
Admission routes1
Has abstractyes

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