Steps Toward Environmental Sustainability in Interventional Radiology
Bibliographic record
Abstract
Environmental degradation and climate change pose an increasingly serious threat to global health, necessitating urgent action to implement environmentally sustainable healthcare practices. Interventional radiology (IR) is a resource-intensive specialty that has not historically emphasized environmental sustainability. This review aims to examine the environmental impact of IR and highlight opportunities for transitioning to more sustainable practices within the IR suite. The environmental impact of IR is assessed in 3 critical domains: (1) energy consumption, (2) waste production, and (3) water pollution. For each domain, actionable strategies are proposed to mitigate environmental harm. Key actions include powering down equipment when not in use, utilizing energysaving modes, minimizing the reliance on single-use items where possible, collaborating with industry to reduce excessive packaging, and implementing recycling programs for waste and iodinated contrast media, along with incorporating environmental sustainability as a quality metric in the departments quality improvement program. Barriers to adopting environmentally sustainable changes include a lack of awareness, financial considerations, and the absence of government, institutional, and industry regulations. Leadership from professional societies and collaboration with industry partners will be essential for driving systemic change. However, individual departments can take action to foster a culture of environmental responsibility and implement sustainable practices.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".