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Record W2020697947 · doi:10.1186/1710-1492-10-s1-a62

Estimating the impact of temperature and air pollution on cardiopulmonary and diabetic health during the TORONTO 2015 Pan Am/Parapan Am Games

2014· article· en· W2020697947 on OpenAlexaffvenueabout
Laura Y. Feldman, Jingqin Zhu, Jacqueline Simatovic, Teresa To

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2014
Typearticle
Languageen
FieldMedicine
TopicTravel-related health issues
Canadian institutionsHospital for Sick ChildrenInstitute for Clinical Evaluative SciencesSickKids FoundationUniversity of Toronto
Fundersnot available
KeywordsAir pollutionEnvironmental scienceCardiovascular healthPollutionMedicineEnvironmental healthInternal medicineChemistry

Abstract

fetched live from OpenAlex

The TORONTO 2015 Pan Am/Parapan Am Games will attract thousands of visitors to Ontario, many of whom may suffer from chronic disease. It has been shown that those with asthma, asthma-related conditions, hypertension and diabetes are particularly sensitive to worsening air quality [ 1 ]. To predict patterns of temperature, humidity and air quality, as well as health service use for cardiopulmonary conditions and diabetes in July 2015. Exposure data (temperature, humidity and air pollution) were obtained from Environment Canada for years 2003 to 2010. Using ArcGIS, the geospatial patterns of exposures were described for regions of Ontario hosting Pan Am events. A linear trend was used to forecast expected exposures for Pan Am regions in July 2015. Health outcomes (hospitalizations, emergency department visits and outpatient claims) for all-cause morbidity, asthma, asthma-related conditions, diabetes and hypertension were measured using data provided by the Institute for Clinical Evaluative Sciences. Associations between exposures and health outcomes were obtained from regression models. Health outcomes were predicted for July 2015 using scenarios of 5% and 10% higher exposure levels than forecasted. Figure 1 shows the geospatial differences in temperature, humidity and air quality across Pan Am regions of Ontario in July 2010. Predicted daily rates of hospitalization and outpatient claims showed the largest increase under scenarios of increased exposure levels (Table 1 ). Given a 10% higher temperature than forecasted, predicted daily outpatient claims rates were 15% higher for all causes (Table 1 ), 20% higher for asthma and 20% higher for hypertension, compared to predicted rates using the forecasted temperature. Given a 10% higher Air Quality Health Index (AQHI) level than forecasted, predicted daily hospitalization rates were 6% higher for all causes (Table 1 ), 4% higher for asthma and 4% higher for asthma-related conditions, compared to predicted rates using the forecasted AQHI level. The geospatial distribution of temperature, humidity and Air Quality Health Index (AQHI) in Pan Am and Parapan Am regions of Ontario in July 2010. A composite measure of NO 2 , PM 2.5 and O 3 where 1-3=low health risk, 4-6=medium health risk, 7-10=high health risk. With thousands more people being exposed to Ontario’s weather and air pollution in July 2015, it is especially important to consider strategies to minimize the environmental impact of human activities. This will lessen the potential burden on individuals, especially those living with chronic disease.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.195

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.344
Teacher spread0.330 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations4
Published2014
Admission routes3
Has abstractyes

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