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Record W2195728399 · doi:10.1586/14779072.2016.1129899

Preventing pediatric cardiomyopathy: a 2015 outlook

2015· review· en· W2195728399 on OpenAlexaff
Paul F. Kantor, Jake A. Kleinman, Thomas D. Ryan, Ivan Wilmot, Warren A. Zuckerman, Linda J. Addonizio, Melanie D. Everitt, John L. Jefferies, Teresa M. Lee, Jeffrey A. Towbin, James D. Wilkinson, Steven E. Lipshultz

Bibliographic record

VenueExpert Review of Cardiovascular Therapy · 2015
Typereview
Languageen
FieldMedicine
TopicCardiomyopathy and Myosin Studies
Canadian institutionsStollery Children's Hospital
Fundersnot available
KeywordsMedicineCardiomyopathyDiseaseIntensive care medicinePopulationClinical trialGenetic testingPathologyHeart failureCardiologyInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Cardiomyopathies in children encompass a broad range of diseases, both genetic and acquired, which manifest as a primary cardiac disorder or as a cardiomyopathy secondary to systemic disease. The burden of this group of disorders is substantial, and growing on a global scale. The availability of disease altering treatments is limited, and therefore a focused review on the prevention of cardiomyopathies is justified. In this review, we address the prevention of cardiomyopathy in children by dealing with the root causes of disease at a molecular, clinical and population level. Recent years have yielded promising returns in basic research related to gene-targeted therapy, specific anti-viral therapies and modification of the effects of cardiotoxic drugs. Much work remains to be done in the fields of vaccine development, public health and adoption of available treatments. Effective research in this field will require that diagnostic methods are both refined, and made available more broadly, from imaging to gene testing. Much of our knowledge today is derived from the use of registries, which have successfully catalogued the detailed phenotype of affected patients, and provided long-term longitudinal follow up of affected individuals.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesMeta-epidemiology (broad)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.844
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0150.019
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.064
GPT teacher head0.386
Teacher spread0.322 · 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; both teacher heads agree on what is shown here.

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

Citations6
Published2015
Admission routes1
Has abstractyes

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