Between Bench and Bedside: Building Clinical Consensus at the NIH, 1977–2013
Bibliographic record
Abstract
After World War II, the National Institutes of Health (NIH) emerged as a major patron of biomedical research. In the succeeding decades, NIH administrators sought to determine how best to disseminate the findings of the research it supported and manage their relationship with clinicians in the national community. This task of bridging research and practice fell to the Office of Medical Applications of Research (OMAR), which administered the NIH Consensus Development Program (CDP) between 1978 and 2012. This article argues that the CDP represented an unusual attempt to depoliticize biomedical research and medical practice at a particularly controversial time in American medicine. Throughout the program's existence, administrators sought ways to bring new knowledge to the medical community without creating the appearance of regulating clinical practice. For an agency with a mandate to promote the production of new biomedical knowledge, the question remained open as to how far this responsibility extended from the bench to the bedside. In striking this balance, the leadership sought to refine their understanding of the role and mission of the NIH. The history of the CDP has much to tell us about postwar biomedical research, health politics, and the institutional development of the NIH.
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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 | 0.078 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.021 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".