Perspective on precision medicine in paediatric heart failure
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it