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Record W4409707137 · doi:10.3390/ijms26093965

Autosomal Dominant Polycystic Kidney Disease-Related Multifocal Renal Cell Carcinoma: A Narrative Iconographic Review

2025· review· en· W4409707137 on OpenAlexafffund
Consolato Sergi, Luis Guerra, Josef Häger

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and Kidney Cyst Diseases
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
FundersHubei UniversityNatural Science Foundation of Hubei ProvinceAustrian Science FundChildren's Health Research InstituteWomen and Children's Health Research InstituteCanadian Institutes of Health ResearchHubei University of TechnologyCancer Research SocietyChildren's Hospital FoundationStollery Children’s Hospital FoundationCancer Research Institute
KeywordsAutosomal dominant polycystic kidney diseasePKD1MedicineDiseaseRenal cell carcinomaPolycystic kidney diseasePathologyKidney diseaseKidneyInternal medicineCancer researchOncology

Abstract

fetched live from OpenAlex

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is the most common inheritable disease of cystic degeneration in the kidney. ADPKD is a significant cause of end-stage renal disease (ESRD). Autosomal Dominant Polycystic Liver Disease (ADPLD) results in substantial PLD with minimal PKD. Currently, there are eight genes which have been associated with ADPKD (PKD1 and PKD2), ADPLD (PRKCSH, SEC63, LRP5, ALG8, and SEC61B), or both (GANAB). The severity of ADPKD can show an extremely broad range, but the evolution to ESRD is doubtless unavoidable. In some patients, carcinogenesis develops with inflammation as a potential promoting factor. In this chapter, we illustrate the severity of ADPKD and the fate to develop renal cell carcinoma (RCC).

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.012
GPT teacher head0.302
Teacher spread0.291 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2025
Admission routes2
Has abstractyes

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Same venueInternational Journal of Molecular SciencesSame topicGenetic and Kidney Cyst DiseasesFrench-language works237,207