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Record W2199198784 · doi:10.1002/ejhf.429

October 2015 at a glance

2015· article· en· W2199198784 on OpenAlexaff
Marco Metra

Bibliographic record

VenueEuropean Journal of Heart Failure · 2015
Typearticle
Languageen
FieldMedicine
TopicPotassium and Related Disorders
Canadian institutionsSurgical Specialties (Canada)
Fundersnot available
KeywordsMedicineHeart failureInternal medicineCardiologyEjection fractionHeart failure with preserved ejection fractionInsulin resistanceInsulin

Abstract

fetched live from OpenAlex

Differences between patients with heart failure (HF) are one of the main obstacles to treatment. The distinction between HF with reduced ejection fraction (HFrEF) or preserved ejection fraction (HFpEF) is a major example of such heterogeneity with effective treatments proven only in HFrEF. In this issue of the journal, a study from the TIME-CHF trial shows differences in the biomarkers' profile between patients with HFrEF and HFpEF with lower NT-proBNP and hs-troponin T levels and higher ST2, hsCRP and cystatin-C levels in the patients with HFpEF.1 As pointed out in the editorial by Eugene Braunwald, these data suggest that different pathophysiological pathways may be involved in the patients with HF. Their detection through biomarkers' profile may be a step towards personalized care.2 Another example of differences between patients with HFrEF and HFpEF is shown. Scherbakow et al. have studied insulin resistance in non-diabetic patients with HF, compared with normal subjects. Patients with HF have insulin resistance and it is more severe in the patients with low EF, compared with those with HFpEF, when dynamic measurements were used.3 Hyperkaelemia may develop in a meaningful proportion of patients with HF and limit the use of renin angiotensin system inhibitors and aldosterone antagonists.4 New gastrointestinal potassium binders have been recently developed and, as pointed out by Marvin Konstam's in his editorial comment, will soon allow safe and tolerable treatment of hypekalemia and better use of neurohormonal antagonists for the treatment of HF.5 In this issue of the journal, the effects of these two agents, sodium zirconium cyclosilicate, ZS-9, a selective potassium ion binder, and patiromer, an oral potassium binding polymer, on serum potassium levels in the patients with HF enrolled in two larger phase III studies, are reported.6, 7 The efficacy, safety and slight differences between these agents can be assessed. The new option for hyperkalaemia treatment is shown. Other studies regarding HF treatment include the revised statistical analysis plan and the baseline characteristics of the patients enrolled in the ATMOSPHERE trial8 and a study comparing the effects of baroreflex activation therapy in patients with and without CRT.9 Enjoy reading!

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.001

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 head0.018
GPT teacher head0.262
Teacher spread0.243 · 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.

Study designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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