Considerations for Implementing Precision Therapeutics for Children
Bibliographic record
Abstract
Improving the utilization of pharmacologic agents in the pediatric population yields significant, perhaps life-long, benefits. Genetic factors related to the disposition of a medication or an alteration at the target receptor site contributes to the observed variability of exposure and response between individuals. An additional source of this variability specific to the pediatric population is ontogeny, where age-specific changes during development may require dose adjustments to obtain the same levels of drug exposure and response. With significant improvements in characterizing both the ontogeny and genetic contributions of drug metabolizing enzymes, the time is right to begin placing more emphasis on response rather than only the dose-exposure relationship. The amount of drug target receptors and the relative affinity for binding at that target site may require different levels of systemic exposure to achieve a desired response. Concentration-controlled studies can identify the needed exposure for a response at the drug target, the level of expression of the target site in an individual patient, and the tools required to individualize response. Although pediatrics represents a large spectrum of growth and development, developing tools to improve drug delivery for each individual patient across the spectrum of the ages treated by clinicians remains valuable.
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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 | 0.044 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.017 | 0.018 |
| Insufficient payload (model declined to judge) | 0.034 | 0.009 |
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".