Identifying undiagnosed asthma in symptomatic adults with normal pre- and post-bronchodilator spirometry
Bibliographic record
Abstract
Background: Some patients with asthma demonstrate normal spirometry and remain undiagnosed without further testing. The objective of this study was to determine clinical predictors of asthma in symptomatic adults with normal spirometry. Methods: Using random-digit dialing and population-based case-finding, we recruited adults from the community with respiratory symptoms and no previous history of lung disease. Participants with normal pre- and post-bronchodilator spirometry subsequently underwent bronchial challenge testing. Asthma was defined as a methacholine provocative concentration (PC20) of < 8 mg/mL. Univariate analyses identified predictive variables, which were then used to construct a multivariate logistic regression model to predict asthma. Model sensitivity, specificity, and area under the receiver operating curve (AUC) were calculated. Results: Of 132 symptomatic individuals with normal spirometry, 26% had asthma. Univariate analyses demonstrated that 4 variables were predictive of asthma: female sex, FEV1 percentage predicted, Percentage Change in FEV1 post-bronchodilator, and answering 9yes9 when asked about symptoms of cough, chest tightness, or wheezing provoked by exercise or cold air. The multivariate model yielded an AUC of 0.82 (95% CI 0.72-0.91), a sensitivity of 82%, and a specificity of 66%. Conclusions: Four readily available patient characteristics demonstrated high sensitivity and AUC for predicting undiagnosed asthma in adults with normal pre- and post-bronchodilator spirometry. These characteristics can help clinicians to decide which symptomatic individuals with normal spirometry should be investigated with bronchial challenge testing.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".