Relationship Between Indoor Environment and Asthma and Wheeze Severity Among Rural Children and Adolescents
Bibliographic record
Abstract
Few studies have examined the associations between environmental characteristics and asthma severity among children in a rural setting. The authors studied these associations using a number of asthma severity indicators. They conducted a case-control study of 6- to 18-year-old children and adolescents in Humboldt, Saskatchewan, and the surrounding area. Only cases representing subjects reporting wheeze in the past 12 months or doctor-diagnosed asthma were used for the present analysis (n = 98). Data were collected by questionnaire, while vacuumed dust (mattress and play area floor) was used for the quantification of endotoxin exposure, and saliva was used for the measurement of cotinine to assess tobacco smoke exposure. Severity indicators included wheeze frequency, breathing medication use, sleep disruption from wheeze, and school absenteeism, all in the past 12 months. A majority of cases were male (62.3%). Wheezing 1 to 3 times was reported by 40.8% of cases, whereas 17.3% wheezed 4 or more times in the past 12 months. Short-acting beta agonist medications or inhaled corticosteroids alone were used by 24.5% of the cases, whereas 33.7% of the cases used multiple or additional breathing medications. Sleep disruption was reported by 28.6% of the cases, whereas 12.2% reported at least one school absence. High tobacco smoke exposure was associated with increased wheeze frequency. There was an inverse association between play area endotoxin concentration and school absenteeism, with some indication of interaction with tobacco smoke exposure. House-cleaning behaviors and changes in health behaviors resulting from the child's respiratory condition were different between those with and without report of sleep disruption due to wheeze. Several environmental variables were associated with severity indicators. However, the associations were not consistent between indicators, suggesting that other factors or changes in behavior resulting from the disease should be considered when assessing these associations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".