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Record W1979087999 · doi:10.3200/aeoh.58.10.613-616

Possible Association between Passive Smoking and Lower Exhaled Nitric Oxide in Asthmatic Children

2003· article· en· W1979087999 on OpenAlexaff
T.J. Warke, Vivienne Mairs, P.S. Fitch, Madeleine Ennis, Michael D. Shields

Bibliographic record

VenueArchives of Environmental Health An International Journal · 2003
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsExhaled nitric oxideMedicineTobacco smokePassive smokingAsthmaExhalationExpirationPediatricsInternal medicineAnesthesiaSpirometryEnvironmental healthRespiratory system

Abstract

fetched live from OpenAlex

In adults, both active and passive smoking reduce levels of exhaled nitric oxide (eNO); however, to date, passive exposure to environmental tobacco smoke (ETS) has not been shown to affect eNO in children. The authors recruited 174 asthmatic children (96 male, 78 female) and 79 nonasthmatic controls (46 male, 33 female) from a group of children aged 5 to 14 yr who attended a children's hospital for an outpatient visit or elective surgery. Each subject's exposure to ETS was ascertained by questionnaire, and their eNO levels were measured. Asthmatic children had higher eNO levels (ppb) than nonasthmatic children (p = 0.04), and asthmatic children exposed to ETS had significantly lower eNO levels than unexposed children (p = 0.005). Exposure to ETS did not alter eNO levels in nonasthmatic children (p = 0.4). Results of the study suggest that ETS exposure is associated with lower eNO levels among childhood asthmatics. Consequently, ETS exposure may need to be considered when physicians interpret eNO levels in asthmatic children. Further study of the effects of ETS on eNO levels is recommended.

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)0.004
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.274
Teacher spread0.266 · 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

Citations22
Published2003
Admission routes1
Has abstractyes

Explore more

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