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Record W2802936789 · doi:10.1097/jom.0000000000001334

Cardiorespiratory Effects of Air Pollution in a Panel Study of Winter Outdoor Physical Activity in Older Adults

2018· article· en· W2802936789 on OpenAlexaff
David M. Stieb, Robin Shutt, Lisa Marie Kauri, Gail Roth, Mieczysław Szyszkowicz, Nina A. Dobbin, Li Chen, Marc Rigden, Keith Van Ryswyk, Ryan Kulka, Branka Jovic, Marie Mulholland, Ling Liu, Guillaume Pelletier, Scott Weichenthal, Robert Dales

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsNOSM UniversityHealth Canada
FundersMinistry of Environment
KeywordsMedicineCardiorespiratory fitnessBlood pressureHeart rateSpirometryInterquartile rangeExhaled nitric oxideCardiologyInternal medicinePhysical therapyAsthma

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to assess cardiorespiratory effects of air pollution in older adults exercising outdoors in winter. METHODS: Adults 55 years of age and older completed daily measurements of blood pressure, peak expiratory flow and oximetry, and weekly measurements of heart rate variability, endothelial function, spirometry, fraction of exhaled nitric oxide and urinary oxidative stress markers, before and after outdoor exercise, for 10 weeks. Data were analyzed using linear mixed effect models. RESULTS: Pooled estimates combining 2014 (n = 36 participants) and 2015 (n = 34) indicated that an interquartile increase in the Air Quality Health Index was associated with a significant (P < 0.05) increase in heart rate (0.33%) and significant decreases in forced expiratory volume (0.30%), and systolic (0.28%) and diastolic blood pressure (0.39%). CONCLUSION: Acute subclinical effects of air pollution were observed in older adults exercising outdoors in winter.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

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 science0.0000.000
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 head0.031
GPT teacher head0.321
Teacher spread0.290 · 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

Citations26
Published2018
Admission routes1
Has abstractyes

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