MétaCan
Menu
Back to cohort
Record W4385429163 · doi:10.31486/toj.23.0036

Massive Renal Cyst Displacing Intra-Abdominal Structures

2023· article· en· W4385429163 on OpenAlexfundno aff
Khalid M.G. Mohammed, Ayaa Zarm, Juan Carlos Q. Velez, Muner Mohamed

Bibliographic record

VenueOchsner Journal · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRenal and related cancers
Canadian institutionsnot available
FundersOtsuka PharmaceuticalOchsner HealthMallinckrodt Pharmaceuticals
KeywordsMedicineAbdominal distensionAbdominal painCreatinineCystHyperkalemiaKidney diseaseSurgeryRenal functionKidneyRadiologyInternal medicine

Abstract

fetched live from OpenAlex

Background: Simple renal cysts typically produce no symptoms or signs and are usually detected incidentally on imaging studies for unrelated causes. Massive renal cysts are very rare. Case Report: A 77-year-old female with preexisting chronic kidney disease presented to our hospital for evaluation of hyperkalemia, abdominal distension, and right flank pain. Upon arrival, her vital signs and physical examination were normal. Laboratory data were pertinent for a serum creatinine of 4.8 mg/dL (6 months prior to presentation, serum creatinine was 1.5 mg/dL, and 1 month after discharge, it was 4.6 mg/dL), and hyperkalemia of 6.0 mmol/L. Computed tomography revealed a massive right renal cyst measuring 22 × 11 × 17.5 cm and displacing the intra-abdominal structures. Because of her symptoms, the patient was evaluated by urology for surgical management. The patient refused invasive procedures and chose pain control and monitoring. Conclusion: Noninvasive treatment options for a massive simple renal cyst are limited. Symptomatic treatment and monitoring the cyst size on a regular basis might be helpful for patients who refuse invasive treatment.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.243
Teacher spread0.238 · 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 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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueOchsner JournalSame topicRenal and related cancersFrench-language works237,207