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Record W1990688519 · doi:10.4061/2011/732746

Hyponatremia and Congestive Heart Failure: A Marker of Increased Mortality and a Target for Therapy

2011· article· en· W1990688519 on OpenAlexaff
Adam Romanovsky, Sean M. Bagshaw, Mitchell H. Rosner

Bibliographic record

VenueInternational Journal of Nephrology · 2011
Typearticle
Languageen
FieldMedicine
TopicElectrolyte and hormonal disorders
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHyponatremiaMedicineHeart failureVolume overloadVasopressinIntensive care medicineVasopressin AntagonistsDiureticClinical trialIntravascular volume statusTolvaptanCardiologyInternal medicineAcute decompensated heart failureReceptorAntagonistHemodynamics

Abstract

fetched live from OpenAlex

Heart failure is one of the most common chronic medical conditions in the developed world. It is characterized by neurohormonal activation of multiple systems that can lead to clinical deterioration and significant morbidity and mortality. In this regard, hyponatremia is due to inappropriate and continued vasopressin activity despite hypoosmolality and volume overload. Hyponatremia is also due to diuretic use in an attempt to manage volume overload. When hyponatremia occurs, it is a marker of heart failure severity and identifies patients with increased mortality. The recent introduction of specific vasopressin-receptor antagonists offers a targeted pharmacological approach to these pathophysiological derangements. Thus far, clinical trials with vasopressin-receptor antagonists have demonstrated an increase in free-water excretion, improvement in serum sodium, modest improvements in dyspnea but no improvement in mortality. Continued clinical trials with these agents are needed to determine their specific role in the treatment of both chronic and decompensated heart failure.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.304
Teacher spread0.270 · 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 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

Citations37
Published2011
Admission routes1
Has abstractyes

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