Symptom-Based Questionnaire for Differentiating COPD and Asthma
Bibliographic record
Abstract
BACKGROUND: Many patients with obstructive lung disease (OLD) carry an inaccurate diagnostic label. Symptom-based questionnaires could identify persons likely to need spirometry. OBJECTIVES: We prospectively tested questions derived from a comprehensive literature review and an international Delphi panel to help identify chronic OLD (COPD) in persons with prior evidence of OLD. METHODS: Subjects were recruited via random mailing to primary-care practices in Aberdeen, Scotland, and Denver, Colorado. Persons aged 40 and older reporting any prior diagnosis of OLD or any respiratory medications in the past year were enrolled. Participants answered 54 questions covering demographics and symptoms and underwent spirometry with reversibility testing. A study diagnosis of COPD was defined by fixed airway obstruction as measured by post-bronchodilator FEV(1)/FVC <0.70. We examined ability of individual questions in a multivariate framework to discriminate between persons with and without the study diagnosis of COPD. RESULTS: 597 persons completed all investigations and proceeded to analysis. The list of 54 questions yielded 52 items for analyses, which was reduced to 19 items for entry into a multivariate regression model. Nine items had significant relationships with the study diagnosis of COPD, including increased age, pack-years, worsening cough, breathing-related disability or hospitalization, worsening dyspnea, phlegm quantity, cold going to the chest, and receipt of treatment for breathing. Individual items yielded odds ratios ranging from 0.33 to 20.7. This questionnaire demonstrated a sensitivity of 72.0 and a specificity of 82.7. CONCLUSIONS: A short, symptom-based questionnaire identifies persons more likely to have COPD among persons with prior evidence of OLD.
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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 | 0.002 | 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) | 0.006 | 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".