Pediatric Critical Care and the Climate Emergency: Our Responsibilities and a Call for Change
Bibliographic record
Abstract
Critical care is perhaps one of the most "climate-intensive" divisions of health care. As greenhouse gas emissions continue to rise, the unprecedented threat of climate change has belatedly prompted an increased awareness of critical care's environmental impact. Within our role as pediatric critical care providers, we have a dual responsibility not only to care for children at their most vulnerable, but also to advocate on their behalf. There are clear, demonstrable effects of our worsening climate on the health of children, with the resultant increased burden of pediatric critical illness and disruption to health care systems. From increasing wildfires and their effect on lung health, to the spread of vector-borne diseases such as dengue, and the increased migration of children due to a changing climate, the effects of a changing climate are here, and we are beginning to see the changing epidemiology of pediatric critical illness. Ensuring that the effects of ongoing changes are minimized, including its future effects on child health, requires a multifaceted approach. As part of this review, we will use the Lancet Countdown on Climate Change indicators to explore the impact of pediatric critical care on climate change and the inevitable influence climate change will have on the future practice of pediatric critical care globally.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.024 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".