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Record W4404254340 · doi:10.1504/ijgw.2024.142600

The effects of climate change on respiratory diseases: a literature review

2024· review· en· W4404254340 on OpenAlex
Kristina Marie Scerri, Sarah Cuschieri

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Global Warming · 2024
Typereview
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsWestern University
Fundersnot available
KeywordsClimate changeEnvironmental scienceRespiratory systemIntensive care medicineClimatologyMedicineInternal medicineBiologyEcology

Abstract

fetched live from OpenAlex

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.952
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.046
GPT teacher head0.418
Teacher spread0.372 · 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