The effects of climate change on respiratory diseases: a literature review
Bibliographic record
Abstract
Climate change is an expanding global epidemic, causing shocking effects as it led to a rise in non-communicable diseases (NCDs). Exploring relationships between the effects of climate change and respiratory diseases are significant. The aim of this narrative review is to provide a detailed summary on the impact of climate change on respiratory diseases. A PubMed literature search (2000-2022) was performed using the following keywords, 'climate change', 'respiratory diseases', 'temperature', 'air pollution', 'wildfires', 'floods', 'thunderstorms', 'dust storms', 'asthma', 'pollen', and 'healthcare system'. Heat and cold temperatures, air pollution, wildfires, droughts, thunderstorms and dust storms as well as allergens were found to have a positive association between climate change and respiratory diseases. The impact of climate change on respiratory diseases is detrimental. If adaptive strategies are not implemented, these climatic effects will lead to a higher respiratory burden among the population and healthcare systems, with potential economic downfall, and an uninhabitable world.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".