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Record W4392769351 · doi:10.1016/j.ekir.2024.03.005

Risk Factors for Severe Hypertension and Proteinuria After Treatment With Vascular Endothelial Growth Factor Signaling Pathway Inhibitors Among Patients With Cancer

2024· article· en· W4392769351 on OpenAlexaff
Paul Hanna, Rajesh Anumolu, Shveta S. Motwani, Kevin L. Chen, Nurit Katz‐Agranov, Olivia Green‐Lingren, Osman A Yilmam, Kenar D. Jhaveri, Abhijat Kitchlu, Jaya Kala, Meghan E. Sise, Shruti Gupta

Bibliographic record

VenueKidney International Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicNitric Oxide and Endothelin Effects
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersNational Institutes of HealthNational Institute of Diabetes and Digestive and Kidney DiseasesDana-Farber Cancer Institute
KeywordsMedicineProteinuriaProstacyclinNitric oxideThrombotic microangiopathyVascular endothelial growth factorAngiogenesisInternal medicineVasodilationEndothelial dysfunctionPathophysiology of hypertensionEndocrinologyEndothelin 1MicroangiopathyBlood pressureVEGF receptorsKidneyReceptorDiabetes mellitus

Abstract

fetched live from OpenAlex

Vascular endothelial growth factor signaling pathway inhibitors (VSPIs) treat numerous types of cancers by blocking tumor angiogenesis;1 however, they are associated with both hypertension and proteinuria. Hypertension has been reported in 20% to 90% of patients.2,3 The onset of hypertension can vary from hours to weeks, depending upon the half-life of the VSPI.4 Proposed mechanisms for VSPI-associated hypertension include reduced vasodilatory nitric oxide and prostacyclin, increased peripheral vascular resistance due to increased vasoconstrictive endothelin-1, and development of renal-limited thrombotic microangiopathy.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.007
GPT teacher head0.222
Teacher spread0.214 · 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 designObservational
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

Citations11
Published2024
Admission routes1
Has abstractyes

Explore more

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