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Record W3117236325

Renal Vein Congestion in Cardiorenal Syndrome Type 1: An Analysis of Two Case Reports From a Hemodynamic Perspective

2020· article· en· W3117236325 on OpenAlexvenueno aff
Sajid Mirza, Ahmad Alkhalil, Edward Rojas, Arturo Perez-Peralta, Giselle Alexandra Suero‐Abreu, Njambi Mathenge, Anthony Lucev, Bernard Kim

Bibliographic record

VenueWorld Journal of Nephrology and Urology · 2020
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsCardiorenal syndromeMedicineRenal veinCardiologyHemodynamicsRenal blood flowInternal medicineVeinIntensive care medicinePerfusionCardiac outputKidney
DOInot available

Abstract

fetched live from OpenAlex

Cardiorenal syndrome (CRS) is a complex disease that results from the close relationship between cardiac and renal physiology. We present two cases which illustrate how renal vein congestion (case 1) and reduced cardiac output (case 2) can both lead to CRS type 1. We focused on the hemodynamic differences between these two cases, as we believe that a deep understanding of the pathophysiology and the renal blood flow (RBF) and renal perfusion pressure (RPP) in these two cases is key in the correct management strategy. This report highlights that CRS type 1 must be recognized as an acute process comprised of two distinct mechanisms, due to renal vein congestion or due to low cardiac output, which can present independently or in varying degrees in the same patient. In consequence, the role of renal vein congestion needs to be recognized early in order to adequately tailor patient treatment. World J Nephrol Urol. 2020;9(2):40-44 doi: https://doi.org/10.14740/wjnu410

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)0.001
metaresearch head score (Gemma)0.004
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 score0.006
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.002
Science and technology studies0.0020.002
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0030.002
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 head0.013
GPT teacher head0.279
Teacher spread0.266 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueWorld Journal of Nephrology and UrologySame topicDialysis and Renal Disease ManagementFrench-language works237,207