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Record W1995470811 · doi:10.1016/s1013-7025(09)70026-0

Spirometric Assessment of Pulmonary Function in Road-side Vendors: A Pilot Study

2002· article· en· W1995470811 on OpenAlexaff
Alice Jones, Elizabeth Dean, Sing Kai Lo, Chon‐Kit Kenneth Chan, Raymond K.T. Chan, Rebecca S.Y. Chan, Jonah L.Y. Chung, Carmen K.M. Ho

Bibliographic record

VenueHong Kong Physiotherapy Journal · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSpirometerMedicineSpirometryPulmonary function testingVital capacityCarboxyhemoglobinAirwayContext (archaeology)ExhalationPhysical therapyInternal medicineLung functionAnesthesiaAsthmaDiffusing capacityLungExhaled nitric oxideCarbon monoxide

Abstract

fetched live from OpenAlex

Although much is known about the chronic effects of air pollution on pulmonary function, short-term changes in response to pollution levels over days, weeks and months have been less well documented. Such investigation requires field studies using portable equipment. Therefore, we studied forced vital capacity (FVC), forced expiratory volume in 1 second, and peak expiratory flow rate using a conventional hand-held spirometer, in a sample of Hong Kong roadside vendors (n = 21; age, 48.7 ± 13.4 yr) across 2 days (n = 14), 4 weeks (n = 10), and 3 months (n = 7). In addition, exhaled carbon monoxide was measured, and percent carboxyhemoglobin derived. There was no difference in pulmonary function between a weekday and the weekend. Only FVC decreased over 4 weeks and 3 months compared with initial testing, but this was not associated with pollution level. Our results support that the technology of hand-held spirometry needs to be advanced to detect potential short-term changes in the real world context, in pulmonary function including small airway reactivity and airway closure. Future generations of this technology need to provide the capacity for more detailed spirometry suitable for field studies.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.062
GPT teacher head0.347
Teacher spread0.285 · 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 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

Citations3
Published2002
Admission routes1
Has abstractyes

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