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Record W4405332595 · doi:10.5772/intechopen.1007937

Pathology of the Renin-Angiotensin-Aldosterone System

2024· book-chapter· en· W4405332595 on OpenAlexaff
Aman Singh, Krishna P. Singh

Bibliographic record

VenueIntechOpen eBooks · 2024
Typebook-chapter
Languageen
FieldMedicine
TopicHormonal Regulation and Hypertension
Canadian institutionsWestern University
Fundersnot available
KeywordsRenin–angiotensin systemAldosteroneMedicineInternal medicineBlood pressure

Abstract

fetched live from OpenAlex

The proposed chapter will involve role of different mechanisms in renin-angiotensin system (RAS) leads to regulation of blood pressure and diseases such as hypertension, heart failure and renal disease. We will also discuss how RAS provides potentials therapeutic target in the treatment of hypertension, kidney disease, and heart disease. There are many drugs which affects different mechanisms of the RAS system and accordingly being used to block different targets in different diseases. We will talk about the success and failures of these drugs. At the end we will discuss clinical trials and their outcomes and how an adverse outcome can be modulated.

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)0.000
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0260.016

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 head0.026
GPT teacher head0.249
Teacher spread0.223 · 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
GenreOther

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

Citations3
Published2024
Admission routes1
Has abstractyes

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