Usefulness of FEV<sub>1</sub>/SVC to uncover airflow obstruction in subjects with preserved FEV<sub>1</sub>/FVC
Bibliographic record
Abstract
Background: Forced vital capacity (FVC) may substantially underestimate slow expiratory VC (SVC) in patients with airflow obstruction thereby leading to a “pseudo-normal” FEV1/FVC (i.e., ≥ 0.7 and/or ≥ lower limit of normality (LLN)). It remains unclear in which specific circumstances FEV1/SVC would be helpful to uncover airway obstruction despite preserved FEV1/FVC. Methods: 15,801 consecutive spirometric measurements showing pre-bronchodilator FEV1/SVC < 0.7 and/or Results: Twenty percent (3,031/15,801) of subjects with FEV1/SVC < 0.7 had FEV1/FVC ≥ 0.7. Among those presenting with both ratios < 0.7, 51.8% had FEV1/SVC < LLN but FEV1/FVC ≥ LLN. Most patients diagnosed with airflow obstruction only by FEV1/SVC had mild disease. However, they did present with lower FEF25-75%, higher residual volume and higher specific airway resistance than those with preserved FEV1/FVC (p<0.01). Prevalence of airflow obstruction diagnosed only by FEV1/SVC increased markedly as a function of body mass index (BMI) (e.g., 11.9% in subjects with BMI < 25 kg/m2 to 33.4% in those with BMI > 40 kg/m2; 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/m2 (2.04 (1.88-2.21)) and FEV1 > 75% predicted (1.21 (1.10-1.32)) were associated with airflow obstruction diagnosed only by FEV1/SVC (p<0.001). Conclusion: Compared to FVC, SVC increases the sensitivity of spirometry to detect mild airflow obstruction regardless the defining criterion (<0.7 or 1.
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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 | 0.002 |
| 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 | 0.001 |
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".