EcoRad: sustainable radiology and the ecology of economics
Bibliographic record
Abstract
This review explores the dual meaning of the prefix "eco"-ecology and economics-and the transformative idea of synthesizing the two into a single "eco" framework. This framework gives rise to EcoRad, which blends economic and ecologic principles to optimize radiology practice. EcoRad strives to achieve the triple bottom line by approaching economic challenges from a planetary health perspective and by using economic approaches to enhance planetary health. In effect, this expands the traditional focus on financial performance to also include social and environmental impact. With EcoRad as a guide, radiology departments are called upon to consider 5 actions that can help overcome barriers to sustainable radiology: adopt sustainable procurement and maintenance, integrate green information technology (IT) and operational efficiencies, advocate for payment models that reward green radiology, champion green budgeting, and involve patients, industry, third-party payors, and policymakers in sustainability.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".