Creating a Distinct Medication-Use System for Children at the Point of Care: The Time is Now
Bibliographic record
Abstract
Children need a distinct medicines-use system designed explicitly for them since their continued inclusion in a system of prescription processing developed for adults generates insoluble risk points and workarounds. The American Academy of Pediatrics (AAP), in its policy statement released by the AAP Committee on Drugs in early 2014 about off-label use in children, posits that federal legislation on increased drug testing in children has been effective, as "there have been over 500 pediatric-specific labeling changes." However, the AAP's position has not changed materially since the original 2002 policy statement. Indeed, other health professionals, their organizations, or affiliated practice-based research network (PBRNs) mechanisms continue to be excluded from consideration, collaboration, or even honorable mention. It is noteworthy that most of the 500 labeling changes made since 1997 have addressed the scientific validity of indications for medication use in pediatric population without regard to pharmacotherapy formulation or monitoring. Medication use in children continues to be associated with an unacceptably high rate of adverse events, morbidity, and death. Children should no longer be "shoehorned" into the adult medication-use system, which faces challenges in addressing even the adult population's needs. The time is now to design a multi-phasic, systematic approach to the pharmacotherapy of children. This paper will argue for the establishment of a distinct medication use system for children, a trans-disciplinary system designed thoughtfully and intentionally, not by convention, consensus, or imitation.
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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 | 0.054 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.014 | 0.024 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.008 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".