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Record W2756117607 · doi:10.1111/joim.12654

The vasopressin system: new insights for patients with kidney diseases

2017· review· en· W2756117607 on OpenAlexaff
William F. Clark, Olivier Devuyst, Ronan Roussel

Bibliographic record

VenueJournal of Internal Medicine · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and Kidney Cyst Diseases
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsMedicineTolvaptanKidney diseaseIntensive care medicineAutosomal dominant polycystic kidney diseaseEpidemiologyDiseaseVasopressinVasopressin AntagonistsPopulationMedical prescriptionCopeptinDiabetes mellitusInternal medicineEndocrinologyEnvironmental healthPharmacologyReceptor

Abstract

fetched live from OpenAlex

People with chronic kidney disease (CKD) are at risk of severe outcomes, such as end-stage renal disease or cardiovascular disease, and CKD is a globally increasing health burden with a high personal and economic cost. Despite major progresses in prevention and therapeutics in last decades, research is still needed to reverse this epidemic trend. The regulation of water balance and the state of activation of the vasopressin system have emerged as factors tightly associated with kidney health, in the general population but also in specific conditions; among them, various stages of CKD, diabetes and autosomal dominant polycystic kidney disease (ADPKD). Basic science findings and also epidemiological evidence have justified important efforts towards interventional studies supporting causality, and opening therapeutic avenues. On the basis of recent clinical data, the blockade of V2 vasopressin receptors using tolvaptan in patients with rapidly progressing ADPKD has been granted in several countries, and a long-term randomized trial evaluating the effect of an increase in water intake in patients with CKD is on-going.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.547
Threshold uncertainty score0.585

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
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 head0.022
GPT teacher head0.320
Teacher spread0.298 · 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 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

Citations19
Published2017
Admission routes1
Has abstractyes

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