Usefulness of FEV<sub>1</sub>/SVC to uncover airflow obstruction in subjects with preserved FEV<sub>1</sub>/FVC
Bibliographic record
Abstract
<b>Background:</b> Forced vital capacity (FVC) may substantially underestimate slow expiratory VC (SVC) in patients with airflow obstruction thereby leading to a “pseudo-normal” FEV<sub>1</sub>/FVC (i.e., ≥ 0.7 and/or ≥ lower limit of normality (LLN)). It remains unclear in which specific circumstances FEV<sub>1</sub>/SVC would be helpful to uncover airway obstruction despite preserved FEV<sub>1</sub>/FVC. <b>Methods:</b> 15,801 consecutive spirometric measurements showing pre-bronchodilator FEV<sub>1</sub>/SVC < 0.7 and/or <LLN. <b>Results:</b> Twenty percent (3,031/15,801) of subjects with FEV<sub>1</sub>/SVC < 0.7 had FEV<sub>1</sub>/FVC ≥ 0.7. Among those presenting with both ratios < 0.7, 51.8% had FEV<sub>1</sub>/SVC < LLN but FEV<sub>1</sub>/FVC ≥ LLN. Most patients diagnosed with airflow obstruction only by FEV<sub>1</sub>/SVC had mild disease. However, they did present with lower FEF<sub>25-75%</sub>, higher residual volume and higher specific airway resistance than those with preserved FEV<sub>1</sub>/FVC (p<0.01). Prevalence of airflow obstruction diagnosed only by FEV<sub>1</sub>/SVC increased markedly as a function of body mass index (BMI) (e.g., 11.9% in subjects with BMI < 25 kg/m<sup>2</sup> to 33.4% in those with BMI > 40 kg/m<sup>2</sup>; p<0.05)). In fact, logistic regression analysis revealed that age < 60 yrs (odds ratio (95% confidence interval)= 1.36 (1.25-1.48)), BMI > 30 kg/m<sup>2</sup> (2.04 (1.88-2.21)) and FEV<sub>1</sub> > 75% predicted (1.21 (1.10-1.32)) were associated with airflow obstruction diagnosed only by FEV<sub>1</sub>/SVC (p<0.001). <b>Conclusion:</b> Compared to FVC, SVC increases the sensitivity of spirometry to detect mild airflow obstruction regardless the defining criterion (<0.7 or <LLN). Slow VC maneuvers are particularly helpful to uncover airflow obstruction in younger and obese subjects with largely preserved FEV<sub>1</sub>.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".