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Record W4312052646 · doi:10.1093/eurheartjsupp/suac120

Getting ahead of the game: in-hospital initiation of HFrEF therapies

2022· article· en· W4312052646 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEuropean Heart Journal Supplements · 2022
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsTed Rogers Centre for Heart ResearchUniversity Health Network
Fundersnot available
KeywordsMedicineDecompensationGuidelineHeart failureIntensive care medicineEjection fractionPharmacotherapyCardiologyInternal medicinePathology

Abstract

fetched live from OpenAlex

Hospitalizations for heart failure (HF) have become a global problem worldwide. Each episode of HF decompensation may lead to deleterious short- and long- term consequences, but on the other hand is an unique opportunity to adjust the heart failure pharmacotherapy. Thus, in-hospital and an early post-discharge period comprise an optimal timing for initiation and optimization of the comprehensive management of HF. This timeframe affords clinicians an opportunity to up titrate and adjust guideline-directed medical therapies (GDMT) to potentially mitigate poor outcomes associated post-discharge and longer-term. This review will cover this timely concept, present the data of utilization of GDMT in HF populations, discuss recent evidence for in-hospital initiation and up-titration of GDMT with a need for post-discharge follow-up and implementation this into clinical practice in patients with heart failure and reduced ejection fraction.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
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)0.0010.000

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 head0.027
GPT teacher head0.295
Teacher spread0.268 · 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