Symptom-Based Questionnaire for Identifying COPD in Smokers
Bibliographic record
Abstract
BACKGROUND: Symptom-based questionnaires may enhance chronic obstructive pulmonary disease (COPD) screening in primary care. OBJECTIVES: We prospectively tested questions to help identify COPD among smokers without prior history of lung disease. METHODS: Subjects were recruited via random mailing to primary care practices in Aberdeen, UK, and Denver, Colo., USA. Current and former smokers aged 40 or older with no prior respiratory diagnosis and no respiratory medications in the past year were enrolled. Participants answered questions covering demographics and symptoms and then underwent spirometry with reversibility testing. A study diagnosis of COPD was defined as fixed airway obstruction as measured by post-bronchodilator FEV(1)/FVC <0.70. We examined the ability of individual questions in a multivariate framework to correctly discriminate between persons with and without COPD. RESULTS: 818 subjects completed all investigations and proceeded to analysis. The list of 54 questions yielded 52 items for analysis, which was reduced to 17 items for entry into multivariate regression. Eight items had significant relationships with the study diagnosis of COPD, including age, pack-years, body mass index, weather-affected cough, phlegm without a cold, morning phlegm, wheeze frequency, and history of any allergies. Individual items yielded odds ratios ranging from 0.23 to 12. This questionnaire demonstrated a sensitivity of 80.4 and specificity of 72.0. CONCLUSIONS: A simple patient self-administered questionnaire can be used to identify patients with a high likelihood of having COPD, for whom spirometric testing is particularly important. Implementation of this questionnaire could enhance the efficiency and diagnostic accuracy of current screening efforts.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".