Derivation and validation of the UCAP-Q case-finding questionnaire to detect undiagnosed asthma and COPD
Bibliographic record
Abstract
BACKGROUND: Many people with asthma and COPD remain undiagnosed. We developed and validated a new case-finding questionnaire to identify symptomatic adults with undiagnosed obstructive lung disease. METHODS: random-digit dialling. Pre- and post-bronchodilator spirometry was used to confirm asthma or COPD. Predictive questions were selected using multinomial logistic regression with backward elimination. Questionnaire performance was assessed using sensitivity, predictive values and area under the receiver operating characteristic curve (AUC). The questionnaire was assessed for test-retest reliability, acceptability and readability. External validation was prospectively conducted in an independent sample and predictive performance re-evaluated. RESULTS: A 13-item Undiagnosed COPD and Asthma Population Questionnaire (UCAP-Q) case-finding questionnaire to predict undiagnosed asthma or COPD was developed. The most appropriate risk cut-off was determined to be 6% for either disease. Applied to the derivation sample (n=1615), the questionnaire yielded a sensitivity of 92% for asthma and 97% for COPD; specificity of 17%; and an AUC of 0.69 (95% CI 0.64-0.74) for asthma and 0.82 (95% CI 0.78-0.86) for COPD. Prospective validation using an independent sample (n=471) showed sensitivities of 93% and 92% for asthma and COPD, respectively; specificity of 19%; with AUCs of 0.70 (95% CI 0.62-0.79) for asthma and 0.81 (95% CI 0.74-0.87) for COPD. AUCs for UCAP-Q were higher compared to AUCs for currently recommended case-finding questionnaires for asthma or COPD. CONCLUSIONS: The UCAP-Q demonstrated high sensitivities and AUCs for identifying undiagnosed asthma or COPD. A web-based calculator allows for easy calculation of risk probabilities for each disease.
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.
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 | 0.024 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".