Relationship between quality of life and clinical status in asthma: a factor analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many studies have shown that correlation between clinical asthma status and asthma-specific quality of life is only weak to moderate. However, this relationship has never been explored to determine whether the weakness is due to noise of measurement or whether quality of life is a distinct component of asthma health status. With a database from three clinical trials (n = 763), factor analysis was used to explore the relationships between quality of life, measured by the Asthma Quality of Life Questionnaire (AQLQ), and conventional measures of asthma clinical status (symptoms, airway calibre and rescue beta2-agonist use). The analysis revealed that although patients with severe, poorly controlled asthma tend to have worse quality of life than milder, well-controlled patients, overall asthma health status has four components (factors): asthma-specific quality of life; airway calibre; daytime symptoms and daytime beta2-agonist use, and night-time symptoms and night-time beta2-agonist use. The clean loading of all 21 outcomes onto four distinct and clinically identifiable factors suggests that, although some weakness of correlation between clinical indices and quality of life may be due to noise of measurement, it is mainly attributable to asthma health status being composed of distinct components.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it