Preventing pediatric cardiomyopathy: a 2015 outlook
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".