From Research to the Bedside: Challenges for Pediatric Academic Researchers
Bibliographic record
Abstract
BACKGROUND: Although improving, development of drugs and devices for children is still less effective than for adults. Pediatric academicians play an important role in the bench-to-bedside research process, but much remains to be done to improve their contributions. OBJECTIVE: To provide a non-comprehensive review of selected literature based on my own personal experience as a U.S. based academic researcher who has spent over 4 decades doing pediatric drug and device development. METHODS: This commentary presents a summary of a talk given at a recent pediatric drug development conference. The observations and conclusions reached were based on the author's (largely US) experience and review of past history, the role of academicians in this process, some successful models of public-private collaboration, available funding, and barriers that remain to be overcome. RESULTS: Pediatric-specific legislation and more available funding have increased participation from and successes of US academicians in the pediatric drug and device development process. Incentive based public-private collaborations have been particularly successful. However, academicians still face both attitude and practical barriers to success. CONCLUSIONS: Changes are needed if academicians are to maximize their involvement in pediatric drug and device development.
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 | 0.119 | 0.213 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.022 | 0.034 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.016 | 0.026 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".