Implementation of the ACT and CAT Questionnaires by Respiratory Specialists
Bibliographic record
Abstract
The Asthma Control Test (ACT) and COPD Assessment Tool (CAT) are validated evidence-based clinical questionnaires to assess symptoms, control, and impact in patients with asthma and COPD, respectively. There is limited data regarding the implementation of these questionnaires in clinical practice by respiratory specialists (providers). This study aimed to characterize the uptake and implementation of ACT and CAT questionnaires amongst respiratory specialists. The primary hypothesis was a high uptake of these tools with limited implementation barriers. A cross-sectional survey was distributed in January 2025 to all respiratory specialists treating asthma or COPD patients across two sites at an academic quaternary centre in Toronto, Canada. Responses regarding their clinical practice, use of the ACT and CAT questionnaires, and barriers towards use were analyzed. Between 11 providers over a 6-month period, ACT completion rate was 2.4% of 1939 asthma patients by 27% of providers, and CAT completion rate was 8.5% of 1806 COPD patients by 18% of providers. There was no association between the use of ACT or CAT and the site, number of patients, years in practice, or use of the other tool. Identified barriers towards use included the administrative burden (ACT - 81.8%, CAT - 63.6%), physical paper burden (ACT - 72.7%, CAT - 45.5%), forgetting to use it (ACT - 45.5%, CAT - 54.5%) and provider preference to use alternate tools or methods (ACT - 45.5%, CAT - 45.5%). 27.3% were unlikely to use the ACT or CAT in the next 6 months. Amongst respiratory specialists, there are low uptake levels of the ACT and CAT questionnaires in clinical practice. This may be improved by addressing the administrative burden of these tools.
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".