Assessment Of Ratio Of Peak Expiratory Flow Rate To Vital Capacity For Identifying Pulmonary Fibrosis
Bibliographic record
Abstract
BACKGROUND: Pulmonary fibrosis (PF) is associated with reduction in vital capacity (VC) and increase in expiratory flow rates, including peak expiratory flow (PEF). Full pulmonary function testing and computed tomography chest scans are limited resources in some geographic areas and a simple and sensitive screening test would be of value. We hypothesized that increase in the ratio of % predicted PEF over % predicted VC (%PEF/%VC), from spirometry alone might be sensitive to screen for pulmonary fibrosis. METHODS: The %PEF/%VC from 1,000 consecutive spirometric flow volume curves was nearly normally distributed: 7.5% (approximately 1.5 standard deviations) had a ratio ≥ 1.4. We evaluated the sensitivity and specificity of this cut point for a diagnosis of PF in a retrospective chart review of 391 patients with good quality spirometry and respirologists' confirmed diagnoses. RESULTS: Of the 391 patients analyzed, 98 had PF, 79 were normal, 70 had a combined obstructive and restrictive processes, 57 had obstructive lung disease, 61 had extra-parenchymal restriction and 26 had non-fibrotic interstitial lung disease. A %PEF/%VC ≥ 1.4 was only 54.1% sensitive in predicting PF, however it had a specificity of 94.9%. There was a 95.1% specificity for ruling in intra-parenchymal opposed to extra-parenchymal restriction. CONCLUSION: A %PEF/%VC ≥ 1.4 was not sensitive enough to screen for PF but did demonstrate high specificity and thus may be helpful in identifying intraparenchymal restriction.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".