Four Opportunities To Revitalize The US Biomedical Research Enterprise
Bibliographic record
Abstract
The US biomedical research enterprise is renowned for its historical and ongoing scientific breakthroughs and advancements. Yet its capacity to solve complex health issues, bridge health equity gaps, and strengthen public trust is constrained by the lack of an overarching national vision, fragmented coordination for research funding, and critical workforce recruitment and retention challenges. To improve national health outcomes and retain global competitiveness, the sector must embrace new approaches. This article, part of the National Academy of Medicine's Vital Directions for Health and Health Care: Priorities for 2025 initiative, identifies four key opportunities to revitalize the biomedical research enterprise: establishing a national advisory body, bolstering the workforce, prioritizing research to reduce health disparities, and developing approaches to streamlining and coordinating federal research funding. These priorities will help the biomedical research enterprise meet twenty-first-century challenges, promote healthy longevity, and preserve US leadership in the global arena.
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 | 0.125 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.025 | 0.031 |
| Insufficient payload (model declined to judge) | 0.014 | 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".