Frontiers of Medical Technology: Reflections on the Intersection of Innovation and the Health Care System
Bibliographic record
Abstract
essay to this issue, he notes that C.P. Snow's observations in his famous lecture, The Two Cultures, still resonate in our society. 1Snow condemned the dominant literary culture's failure to embrace science and the consequences to the "underfed . . .[who] die before their time." 2 Snow yearned for a bridge over the divide, "something like a third culture," comprising a community of social scientists "concerned with how human beings are living or have lived." 3 Recognizing the need for social science to build bridges, University of Minnesota's conference 4 sought to address the intersection between the "life sciences and the political demands and social aspirations of the law." 5 My specific challenge was to explore the interface between innovation in medical technology and the political and social demands of the health care delivery system.This interface is constantly evolving in response to technological innovation.C.P.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.010 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".