Sustainable Radiology: Health Equity and Quality Improvement
Bibliographic record
Abstract
Environmental sustainability in radiology has a growing role in health care as climate changes intensify. Quality Initiative/Improvement (QI) projects lead to improved patient care and safety as well as efficient use of limited health care resources. When designing a QI project, including an environmental lens increases awareness of sustainability in medicine. This document will focus specifically on sustainability in QI (SusQI) in the field of radiology, though similar principles may be applied in other medical fields. The sustainable QI model updates the value equation denominator from cost to the triple bottom line of environmental, social, and economic measures. Using this SusQI model can lead to a win (patient)-win (health care system)-win (environment). This article will also discuss the importance of the environment for human health and the link between quality initiatives and environmental sustainability in demonstrating the value of Radiology and improving the quality of patient care. It will provide some examples of sustainability applied to many quality initiatives in Radiology: For example, reduction in oral contrast use for many previously used indications, streamlined MRI protocols, as well as using ultrasound over CT or MRI for indications that are equally appropriate.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".