Engaging clinicians to reduce carbon-intensive, unnecessary tests and procedures
Bibliographic record
Abstract
Up to 30% of ordered tests and treatments do not add value and can even harm patients.1–3 It stands to reason that eliminating the use of these unnecessary tests and treatments can reduce a component of health care's greenhouse gas emissions, as every test and treatment has a carbon footprint.4 Generally speaking, physicians and other health-care professionals (hereafter referred to as clinicians) do not know how to change their daily practice to mitigate progression of the climate crisis.5,6 In this Comment, we describe a campaign led by national professional societies to develop specific and actionable recommendations that clinicians can implement in their daily practice to decrease their carbon footprint through elimination of carbon-intensive, unnecessary tests and treatments.
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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 | 0.021 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".