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Record W4366238473 · doi:10.1148/radiol.230229

Climate Change and Radiology: Impetus for Change and a Toolkit for Action

2023· review· en· W4366238473 on OpenAlexaff
M. J. Brown, Julia Schoen, Jonathan Gross, Reed A. Omary, Kate Hanneman

Bibliographic record

VenueRadiology · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsMedicineClimate changeIncentiveHealth careEquity (law)Action (physics)Call to actionTechnological changePublic relationsBusinessPolitical scienceMarketingComputer science

Abstract

fetched live from OpenAlex

This special report discusses the importance of climate change for health care and radiology. The impact of climate change on human health and health equity, the contribution of health care and medical imaging to the climate crisis, and the impetus for change within radiology to create a more sustainable future are covered. The authors focus on actions and opportunities to address climate change in our role as radiologists. A toolkit highlights actions we can take toward a more sustainable future, linking each action with the expected impact and outcome. This toolkit includes a hierarchy of actions from first steps to advocating for system-level change. This includes actions we can take in our daily lives, in radiology departments and professional organizations, and in our relationships with vendors and industry partners. As radiologists, we are adept at managing rapid technological change, which makes us ideally suited to lead these initiatives. Alignment of incentives and synergies with health systems are highlighted given that many of the proposed strategies also result in cost savings.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.469
GPT teacher head0.448
Teacher spread0.021 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations100
Published2023
Admission routes1
Has abstractyes

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