Bibliographic record
Abstract
BACKGROUND: Contaminants encountered in many households, such as environmental tobacco smoke, house dust mite, cockroach, cat and dog dander, and mold, are risk factors in asthma. Young children are a particularly vulnerable subpopulation for environmentally mediated asthma, and the economic burden associated with this disease is substantial. Certain mechanical interventions are effective both in reducing allergen loads in the home and in improving asthmatic children's respiratory health. RESULTS: Combinations of interventions including the use of dust mite-impermeable bedding covers, improved cleaning practices, high-efficiency particulate air vacuum cleaners, mechanical ventilation, and parental education are associated with both asthma trigger reduction and improved health outcomes for asthmatic children. Compared with valuated health benefits, these combinations of interventions have proven cost effective in studies that have employed them. Education alone has not proven effective in changing parental behaviors such as smoking in the home. CONCLUSIONS: Future research should focus on improving the effectiveness of education on home asthma triggers, and understanding long-term children's health effects of the interventions that have proven effective in reducing asthma triggers.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".