Evaluating Asthma Control: A Comparison of Measures Using an Item Response Theory Approach
Bibliographic record
Abstract
Self-reported symptoms, FEV(1), and clinician judgment are all used to evaluate asthma control. The relative utility of each measure of control cannot be easily assessed. Item response theory (IRT) approaches allow for the direct comparison of the utility of different types of measures used to assess control. The objective of this study was to evaluate the validity and reliability of evaluating asthma control using symptom, clinical, and physiologic measures by applying an IRT approach. Subjects receiving care at an asthma clinic were evaluated on measures of asthma control. Based on 114 evaluations, IRT parameters were estimated to evaluate whether measures assessed a single underlying construct, the hierarchical relationship between the measures and the level of control each measure assessed, whether measures targeted all levels of asthma control, and whether the scoring categories distinguished between different levels of control. Infit statistics (0.74-1.5) for individual items showed that all items fit the underlying concept of asthma control. The reproducibility of the hierarchal scale was high (0.9). The results also demonstrated that items differentiated two strata (high, low) of control. The gaps in the hierarchal scale showed that for many subjects (37%) there were no items at their level of asthma control. The IRT approach identified gaps in current measurement that need to be addressed to provide more precise evaluations of control required to accurately monitor changes in patient status.
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How this classification was reachedexpand
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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".