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Record W2552242970 · doi:10.1016/j.ekir.2016.11.002

Extreme Renal Pathology in Alagille Syndrome

2016· article· en· W2552242970 on OpenAlexaff
Mei Lin Z. Bissonnette, Jerome C. Lane, Anthony Chang

Bibliographic record

VenueKidney International Reports · 2016
Typearticle
Languageen
FieldMedicine
TopicPediatric Hepatobiliary Diseases and Treatments
Canadian institutionsSt. Paul's Hospital
Fundersnot available
KeywordsAlagille syndromeJAG1MedicineKidneyPathologyRenal dysplasiaCholestasisDysplasiaDesminNotch signaling pathwayInternal medicineImmunohistochemistryReceptor

Abstract

fetched live from OpenAlex

Alagille syndrome (ALGS) results from mutations in JAG1 and NOTCH2 in the Notch signaling pathway.1,2 These mutations clinically manifest in various ways, but ALGS is most commonly characterized by a paucity of bile ducts in the liver. ALGS often involves the kidney, which can be characterized by defects in the glomerular vasculature, podocytes, proximal tubules, and renal dysplasia. In addition, altered lipid metabolism in ALGS can cause mesangial lipidosis in the kidney.1,3 Few case reports describe the renal manifestations of ALGS.

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)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.270
Teacher spread0.253 · 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

Citations12
Published2016
Admission routes1
Has abstractyes

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