Underdiagnosis and Overdiagnosis of Asthma
Bibliographic record
Abstract
Asthma is diagnosed on the basis of respiratory symptoms of wheeze, cough, chest tightness, and/or dyspnea together with physiologic evidence of variable expiratory airflow limitation. The prevalence of asthma varies widely around the world, ranging from 0.2% to 21.0% in adults and from 2.8% to 37.6% in 6- to 7-year-old children. Population-based studies in children, adults, and the elderly suggest that from 20% to 70% of people with asthma in the community remain undiagnosed and hence untreated. Underdiagnosis of asthma has been found to be associated with underreporting of respiratory symptoms by patients to physicians as well as poor socioeconomic status. On the opposite side of the spectrum, studies of patients with physician-diagnosed asthma suggest that 30-35% of adults and children diagnosed with asthma do not have current asthma, suggesting that asthma is also overdiagnosed in the community. Overdiagnosis of current asthma can occur because of physicians' failure to confirm variable airflow limitation at the time of diagnosis or when sustained clinical remission of disease goes unrecognized. In this review, we define under- and overdiagnosis and explore the prevalence and burden of under- and overdiagnosis of asthma both in patients and within healthcare systems. We further describe potential solutions to prevent under- and overdiagnosis of asthma.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".