Perspective on precision medicine in paediatric heart failure
Bibliographic record
Abstract
In 2015, President Obama launched the Precision Medicine Initiative (PMI), which introduced new funding to a method of research with the potential to study rare and complex diseases. Paediatric heart failure, a heterogeneous syndrome affecting approximately 1 in 100000 children, is one such condition in which precision medicine techniques may be applied with great benefit. Current heart failure therapies target downstream effects of heart failure rather than the underlying cause of heart failure. As such, they are often ineffective in paediatric heart failure, which is typically of primary (e.g. genetic) rather than secondary (e.g. acquired) aetiology. It is, therefore, important to develop therapies that can target the causes of heart failure in children with greater specificity thereby decreasing morbidity, mortality and burden of illness on both patients and their families. The benefits of co-ordinated research in genomics, proteomics, metabolomics, transcriptomics and phenomics along with dietary, lifestyle and social factors have led to novel therapeutic and prognostic applications in other fields such as oncology. Applying such co-ordinated research efforts to heart failure constitutes an important step in advancing care and improving the lives of those affected.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.007 |
| 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".