Climate change and global child health: what can paediatricians do?
Bibliographic record
Abstract
A little over a decade ago, the Lancet Climate Commission concluded that anthropogenic climate change threatens to undermine the past 50 years of gains in public health and, conversely, that a comprehensive response to climate change could be ‘the greatest global health opportunity of the 21 st century’.1 In a recent review, experts quantified the impact of climate change on health and estimated that heatwaves between 2000 and 2016 had resulted in 5.3% lower outdoor manual productivity and that economic losses from climate change related events in 2016 alone totalled almost US$129 billion.2 Historically, major excess mortality peaks have been related to extreme weather events, such as the Bangladesh cyclone of 1991, Venezuela floods and mudslides of 1999 and Myanmar cyclone of 2008. These three extreme weather events alone accounted for more than 300000 deaths.2 It is estimated that Hurricane Maria affecting Puerto Rico in 2017 was associated with excess mortality with estimates ranging between 2700 and 4600 deaths.3 4 In addition to direct effects and increased risks of climate change associated disasters such as drought or floods, global climate change has been associated with major changes in infectious diseases risks. The annual numbers of cases of dengue fever have doubled every decade since 1990, with 58.4 million apparent cases in 2013, accounting for more than 10 000 deaths.5 Other infectious diseases, such …
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.028 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".