Clinical Predictors of Health-related Quality of Life Depend on Asthma Severity
Bibliographic record
Abstract
The National Asthma Education and Prevention Program guidelines define asthma severity before treatment by lung function and symptoms. It has been assumed, but not demonstrated, that improvement in these measures would translate into improvement in health-related quality of life (HRQL). Because HRQL is an important outcome in asthma management, we asked what are the determinants of HRQL? To address this question, we retrospectively analyzed HRQL data, as measured by the Juniper Asthma Quality of Life Questionnaire, in subjects with mild versus moderate-severe asthma from two clinical trials. We examined whether these traditional clinical outcomes have different relationships to HRQL depending on asthma severity. We also assessed whether the relationship between clinical outcomes and HRQL in subjects with moderate-severe asthma would change when subjects improved to mild-moderate disease with controller medication treatment. Lung function was not an independent predictor or determinant of HRQL at any level of asthma severity, whereas intensity of shortness of breath predicted HRQL at all levels of asthma severity. Rescue beta-agonist use independently predicted HRQL in subjects with mild asthma, but not in those with moderate-severe asthma. In subjects with moderate-severe asthma who improved to mild-moderate disease with controller treatment, rescue beta-agonist use predicted HRQL. We conclude that the independent determinants of HRQL vary according to asthma severity and change with asthma treatment.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".