Quantification of asthma control: validation of the Asthma Control Scoring System
Bibliographic record
Abstract
BACKGROUND: We developed an instrument for quantifying asthma control, the Asthma Control Scoring System (ACSS), based on the criteria proposed by the Canadian Asthma Consensus Guidelines. OBJECTIVE: To assess the measurement properties of the ACSS. METHODS: The ACSS and two other questionnaires were completed by 44 asthmatic patients on a first visit and 2 weeks later. The ACSS evaluates three types of parameters: clinical, physiologic, and inflammatory. These parameters are each quantified to obtain a maximal score of 100% and a global score is calculated as the mean of these scores. RESULTS: The analysis showed sufficient internal consistency for every section of the ACSS (Cronbach's-alpha ranging from 0.72 to 0.88). Pearson's correlations indicated good test-retest reliability for the clinical score (r = 0.59, P = 0.005), the physiologic score (r = 0.86, P < 0.0001), the inflammatory score (r = 0.71, P = 0.049), and the global score (r = 0.65, P = 0.001). Cross-sectional and longitudinal construct validity were supported by moderate correlations between the ACSS scores and corresponding instruments. CONCLUSIONS: The ACSS is a valid tool for quantifying asthma control parameters, using a percent score. Further research should determine the usefulness of such an instrument as a means to improve asthma management and reduce related morbidity.
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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 | 0.022 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".