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Record W2224731660 · doi:10.1155/2015/361687

Climate Change and the New Normal for Cardiorespiratory Disease

2015· review· en· W2224731660 on OpenAlexaff
Tim K. Takaro, Sarah B. Henderson

Bibliographic record

VenueCanadian Respiratory Journal · 2015
Typereview
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsBC Centre for Disease ControlUniversity of British ColumbiaSimon Fraser University
Fundersnot available
KeywordsCardiorespiratory fitnessClimate changeMedicineEnvironmental healthAffect (linguistics)Natural resource economicsDiseaseEnvironmental planningEnvironmental resource managementEnvironmental scienceEcology

Abstract

fetched live from OpenAlex

Climate change is already affecting the cardiorespiratory health of populations around the world, and these impacts are expected to increase. The present overview serves as a primer for respirologists who are concerned about how these profound environmental changes may affect their patients. The authors consider recent peer-reviewed literature with a focus on climate interactions with air pollution. They do not discuss in detail cardiorespiratory health effects for which the potential link to climate change is poorly understood. For example, pneumonia and influenza, which affect >500 million people per year, are not addressed, although clear seasonal variation suggests climate-related effects. Additionally, large global health impacts in low-resource countries, including migration precipitated by environmental change, are omitted. The major cardiorespiratory health impacts addressed are due to heat, air pollution and wildfires, shifts in allergens and infectious diseases along with respiratory impacts from flooding. Personal and societal choices about carbon use and fossil energy infrastructure should be informed by their impacts on health, and respirologists can play an important role in this discussion.

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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.881
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.199
GPT teacher head0.358
Teacher spread0.159 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations16
Published2015
Admission routes1
Has abstractyes

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