Between-Visit Variability in FEV1 as a Diagnostic Test for Asthma in Adults
Bibliographic record
Abstract
Abstract Rationale The reliability of using between-visit variation in forced expiratory volume in 1 second (FEV1) to diagnose asthma is understudied, and hence uncertain. Objective To determine whether FEV1 variability measured over recurrent visits is significantly associated with a diagnosis of current asthma. Methods Randomly selected adults (N = 964) with a history of physician-diagnosed asthma were studied from 2005 to 2007 and from 2012 to 2016. A diagnosis of current asthma was confirmed in those participants who exhibited bronchial hyperresponsiveness to methacholine and/or acute worsening of asthma symptoms while being weaned off asthma medications. Regression analyses and receiver operating curves were used to evaluate the ability of between-visit FEV1 variability to diagnose asthma. Results A current diagnosis of asthma was confirmed in 584 of 964 participants (60%). Between-visit absolute variability in FEV1 was significantly greater in those in whom current asthma was confirmed, compared with those in whom current asthma was ruled out (7.3% vs. 4.8%; mean difference between the two groups, 2.5%; 95% confidence interval, 1.7–3.3%). However, a 12% and 200-ml between-visit variation in FEV1, which is the diagnostic threshold recommended by Global Initiative for Asthma, exhibited a sensitivity of only 0.17 and a specificity of 0.94 for confirming current asthma. A between-visit absolute variability in FEV1 ≥ 12% and 200 ml increased the pretest probability of asthma from 60% to a posttest probability of 81%. Conclusions A 12% and 200-ml between-visit variation in FEV1, if present, has reasonably good specificity for diagnosing asthma, but has poor sensitivity compared with bronchial challenge testing. Between-visit variability in FEV1 is a relatively unhelpful test to establish a diagnosis 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 | 0.009 | 0.023 |
| 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 | 0.001 |
| 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 | 0.000 |
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".