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New Mechanistic and Therapeutic Targets for Pediatric Heart Failure

2014· article· en· W2021644343 on OpenAlexaff
Kristin M. Burns, Barry J. Byrne, Bruce D. Gelb, Bernhard Kühn, Leslie A. Leinwand, Seema Mital, Gail D. Pearson, Mark D. Rodefeld, Joseph W. Rossano, Brian L. Stauffer, Michael D. Taylor, Jeffrey A. Towbin, Andrew N. Redington

Bibliographic record

VenueCirculation · 2014
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsHospital for Sick Children
FundersNational Heart, Lung, and Blood InstituteNational Institutes of Health
KeywordsMedicineMedical schoolHeart failureSick childGerontologyInternal medicinePediatrics

Abstract

fetched live from OpenAlex

Challenges and Opportunities in Pediatric Heart Failure and Transplantation 79 P ediatric heart failure (HF) is the inability of the heart of an infant, child, or adolescent to meet the body's metabolic demands.It involves circulatory, neurohumoral, and molecular abnormalities that manifest as edema, respiratory distress, growth failure, and exercise intolerance.The myriad causes include inherited and acquired myocardial anomalies (cardiomyopathy [CM]), volume overload (intracardiac shunts, valvular regurgitation), and the unique hemodynamics predicated by a functional single ventricle (palliated complex congenital heart disease [CHD]).Although the societal and financial costs of adult HF are well known, the burden of pediatric HF is less familiar, but no less onerous.New-onset HF requiring hospital admission occurs in 0.87 per 100 000 children, 1 yet that does not include the growing population with CHD-related HF.In 2006, there were nearly 14 000 pediatric hospitalizations for HF from all causes in the United States. 2 The rate of HF-related admissions was nearly 18 per 100 000 children, 2 which is comparable to severe sepsis.3 The mortality for pediatric HF hospitalizations is significant.The 7% overall hospital mortality rate exceeds the 4% mortality of adult HF admissions 4 and represents a 20-fold increase over children without HF. 2 With comorbidities like renal failure, sepsis, or stroke, hospital mortality in pediatric HF can exceed 20%, 2 yet the risk does not end with discharge.After an initial HF hospitalization, only 21% of children in 1 study avoided readmission, death, or transplantation.5 Pediatric HF treatment is resource intensive.Although the total healthcare costs for pediatric HF are lower than for adults, per-patient costs are higher.The estimated hospital charge per pediatric HF admission in 2006 was >$135 000, with aggregate charges exceeding $1.8 billion.6 Certain subpopulations of pediatric HF incurred disproportionally higher costs.For example, single-ventricle CHD averaged >$200 000 per hospitalization, 7 whereas adult HF admissions averaged <$25 000.8 These data do not account for the full burden of pediatric HF.There are no national cost estimates for outpatient pediatric HF management, and, because long-term survival rates are higher in children, the lifetime costs of HF in children are likely to be much higher than in adults.Few HF therapies are developed specifically for children, and drugs that benefit adults have not clearly demonstrated clinical effectiveness in pediatric HF. 9 In fact, pediatric HF therapy has not improved survival significantly over the past 30 years.10 Consequently, we need to understand the mechanisms unique to pediatric HF to inform the development of appropriate therapies. Working GroupIn April 2013, the National Heart, Lung, and Blood Institute convened a Working Group (WG) of experts in pediatric and adult cardiology, HF, CM, cardiomyocyte proliferation, genomics, pediatric cardiac surgery, gene therapy, and imaging.Although the WG acknowledged the need to improve clinical care and quality of life for children with HF, its purpose was to identify promising research targets, or mechanistic areas related to the unique pathogenesis of pediatric HF with possible therapeutic potential.

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)0.006
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0040.001

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 head0.015
GPT teacher head0.248
Teacher spread0.232 · 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
GenreReview

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

Citations51
Published2014
Admission routes1
Has abstractyes

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