Review: Advancing Precision Medicine in Paediatrics; Past, Present and Future — R0/PR2
Bibliographic record
Abstract
Precision Medicine is an approach to disease treatment and prevention taking into account individual genetic, environmental, therapeutic and lifestyle variability for each person. This holistic approach to therapeutics is intended to enhance drug efficacy and safety not only across healthcare systems but for individual patients. While weight and to some extent gestational age have been considered in determining drug dosing in children, historically other factors including genetic variability have not been factored into therapeutic decision making. As our knowledge of the role of ontogeny and genetics in determining drug efficacy and safety has expanded, these insights have provided new opportunities to apply principles of Precision Medicine to the care of infants, children and youth. These opportunities are most likely to be achieved first in select sub-groups of children. While there are many challenges to the successful implementation of Precision Medicine in children including the need to ensure that Precision Medicine enhances rather than reduces equity in children’s health care rather, there are many more opportunities. Research, advocacy, planning and teamwork are required to move Precision Medicine forward in children in pursuit of the common goal of safe and effective drug therapy.
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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.012 |
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".